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Coconuts may be delicious and useful for producing a wide range of products, but harvesting them is no easy task. Specially trained harvesters must risk their lives by climbing trees roughly 15 meters high to hack off just one bunch of coconuts. A group of researchers in India has designed a robot, named Amaran, that could reduce the need for human harvesters to take such a risk. But is the robot up to the task?

The researchers describe the tree-climbing robot in a paper published in the latest issue of IEEE/ASME Transactions on Mechatronics. Along with lab tests, they compared Amaran’s ability to harvest coconuts to that of a 50-year-old veteran harvester. Whereas the man bested the robot in terms of overall speed, the robot excelled in endurance.

To climb, Amaran relies on a ring-shaped body that clasps around trees of varying diameter. The robot carries a control module, motor drivers, a power management unit, and a wireless communications interface. Eight wheels allow it to move up and down a tree, as well as rotate around the trunk. Amaran is controlled by a person on the ground, who can use an app or joystick system to guide the robot’s movements.

Once Amaran approaches its target, an attached controller unit wields a robotic arm with 4 degrees of freedom to snip the coconut bunch. As a safety feature, if Amaran’s main battery dies, a backup unit kicks in, helping the robot return to ground.

Rajesh Kannan Megalingam, an assistant professor at Amrita Vishwa Vidyapeetham University, in South India, says his team has been working on Amaran since 2014. “No two coconut trees are the same anywhere in the world. Each one is unique in size, and has a unique alignment of coconut bunches and leaves,” he explains. “So building a perfect robot is an extremely challenging task.”

“No two coconut trees are the same … So building a perfect robot is an extremely challenging task.” —Rajesh Kannan Megalingam, Amrita Vishwa Vidyapeetham University

While testing the robot in the lab, Megalingam and his colleagues found that Amaran is capable of climbing trees when the inclination of the trunk is up to 30 degrees with respect to the vertical axis. Megalingam says that many coconut trees, especially under certain environmental conditions, grow at such an angle.

Next, the researchers tested Amaran in the field, and compared its ability to harvest coconuts to the human volunteer. The trees ranged from 6.2 to 15.2 m in height.

It took the human on average 11.8 minutes to harvest one tree, whereas it took Amaran an average of 21.9 minutes per tree (notably 14 of these minutes were dedicated to setting up the robot at the base of the tree, before it even begins to climb).

Photo: HuT Labs

But Megalingam notes that Amaran can harvest more trees in a given day. For example, the human harvester in their trials could scale about 15 trees per day before getting tired, while the robot can harvest up to 22 trees per day, if the operator does not get tired. And although the robot is currently teleoperated, future improvements could make it more autonomous, improving its climbing speed and harvesting capabilities. 

“Our ultimate aim is to commercialize this product and to help the coconut farmers,” says Megalingam. “In Kerala state, there are only 7,000 trained coconut tree climbers, whereas the requirement is about 50,000 trained climbers. The situation is similar in other states in India like Tamil Nadu, Andhra, and Karnataka, where coconut is grown in large numbers.”

He acknowledges that the current cost of the robot is a barrier to broader deployment, but notes that community members could pitch together to share the costs and utilization of the robot. Most importantly, he notes, “Coconut harvesting using Amaran does not involve risk for human life. Any properly trained person can operate Amaran. Usually only male workers take up this tree climbing job. But Amaran can be operated by anyone irrespective of gender, physical strength, and skills.”

Back to IEEE Journal Watch

A battle is brewing over the fate of the deep ocean. Huge swaths of seafloor are rich in metals—nickel, copper, cobalt, zinc—that are key to making electric vehicle batteries, solar panels, and smartphones. Mining companies have proposed scraping and vacuuming the dark expanse to provide supplies for metal-intensive technologies. Marine scientists and environmentalists oppose such plans, warning of huge and potentially permanent damage to fragile ecosystems.

Pietro Filardo is among the technology developers who are working to find common ground.

Image: Pliant Energy Systems

His company, Pliant Energy Systems, has built what looks like a black mechanical stingray. Its soft, rippling fins use hyperbolic geometry to move in a traveling wave pattern, propelling the skateboard-sized device through water. From an airy waterfront lab in Brooklyn, New York, Filardo’s team is developing tools and algorithms to transform the robot into an autonomous device equipped with grippers. Their goal is to pluck polymetallic nodules—potato-sized deposits of precious ores—off the seafloor without disrupting precious habitats.

“On the one hand, we need these metals to electrify and decarbonize. On the other hand, people worry we’re going to destroy deep ocean ecosystems that we know very little about,” Filardo said. He described deep sea mining as the “killer app” for Pliant’s robot—a potentially lucrative use for the startup’s minimally invasive design.

How deep seas will be mined, and where, is ultimately up to the International Seabed Authority (ISA), a group of 168 member countries. In October, the intergovernmental body is expected to adopt a sweeping set of technical and environmental standards, known as the Mining Code, that could pave the way for private companies to access large tracts of seafloor. 

The ISA has already awarded 30 exploratory permits to contractors in sections of the Atlantic, Pacific, and Indian Oceans. Over half the permits are for prospecting polymetallic nodules, primarily in the Clarion-Clipperton Zone, a hotspot south of Hawaii and west of Mexico.

Researchers have tested nodule mining technology since the 1970s, mainly in national waters. Existing approaches include sweeping the seafloor with hydraulic suction dredges to pump up sediment, filter out minerals, and dump the resulting slurry in the ocean or tailing ponds. In India, the National Institute of Ocean Technology is building a tracked “crawler” vehicle with a large scoop to collect, crush, and pump nodules up to a mother ship.

Mining proponents say such techniques are better for people and the environment than dangerous, exploitative land-based mining practices. Yet ocean experts warn that stirring up sediment and displacing organisms that live on nodules could destroy deep sea habitats that took millions of years to develop. 

“One thing I often talk about is, ‘How do we fix it if we break it? How are we going to know we broke it?’” said Cindy Lee Van Dover, a deep sea biologist and professor at Duke University’s Nicholas School of the Environment. She said much more research is required to understand the potential effects on ocean ecosystems, which foster fisheries, absorb carbon dioxide, and produce most of the Earth’s oxygen.

Significant work is also needed to transform robots into metal collectors that can operate some 6,000 meters below the ocean surface.

Photo: Pliant Energy Systems Former Pliant engineers Daniel Zimmerman (right) and Michael Weaker work on a prototype that harnesses energy from rivers and streams—the precursor to Velox.

Pliant’s first prototype, called Velox, can navigate the depths of a swimming pool and the shallow ocean “surf zone” where waves crash into the sand. Inside Velox, an onboard CPU distributes power to actuators that drive the undulating motions in the flexible fins. Unlike a propeller thruster, which uses a rapidly rotating blade to move small jets of water at high velocity, Pliant’s undulating fins move large volumes of water at low velocity. By using the water’s large surface area, the robot can make rapid local maneuvers using relatively little battery input, allowing the device to operate for longer periods before needing to recharge, Filardo said. 

The design also stirs up less sediment on the seafloor, a potential advantage in sensitive deep sea environments, he added.

The Brooklyn company is partnering with the Massachusetts Institute of Technology to develop a larger next-generation robot, called C-Ray. The highly maneuverable device will twist and roll like a sea otter. Using metal detectors and a mix of camera hardware and computer algorithms, C-Ray will likely be used to surveil the surf zone for potential hazards to the U.S. Navy, who is sponsoring the research program.

Illustration: Pliant Energy Systems A conceptual illustration of C-Ray robots collecting deep sea polymetallic nodules.

The partners ultimately aim to deploy “swarms” of autonomous C-Rays that communicate via a “hive mind”—applications that would also serve to mine polymetallic nodules. Pliant envisions launching hundreds of gripper-equipped robots that roam the seafloor and place nodules in cages that float to the surface on gas-filled lift bags. Filardo suggested that C-Ray could also swap nodules with lower-value stones, allowing organisms to regrow on the seafloor.

A separate project in Italy may also yield new tools for plucking the metal-rich orbs.

SILVER2 is a six-legged robot that can feel its way around the dark and turbid seafloor, without the aid of cameras or lasers, by pushing its legs in repeated, frequent cycles.

“We started by looking at what crabs did underwater,” said Marcello Calisti, an assistant professor at the BioRobotics Institute, in the Sant'Anna School of Advanced Studies. He likened the movements to people walking waist-deep in water and using the sand as leverage, or the “punter” on a flat-bottomed river boat who uses a long wooden pole to propel the vessel forward.

Photo: BioRobotics Institute/Sant'Anna School of Advanced Studies

Calisti and colleagues spent most of July at a seaside lab in Livorno, Italy, testing the 20-kilogram prototype in shallow water. SILVER2 is equipped with a soft elastic gripper that gently envelopes objects, as if cupping them in the palm of a hand. Researchers used the crab-like robot to collect plastic litter on the seabed and deposit the debris in a central collection bin.

Although SILVER2 isn’t intended for deep sea mining, Calisti said he could foresee potential applications in the sector if his team can scale the technology.

For developers like Pliant, their ability to raise funding and achieve their mining robots will largely depend on the International Seabed Authority’s next meeting. Opponents of ocean mining are pushing to pause discussions on the Mining Code to give scientists more time to evaluate risks, and to allow companies like Tesla or Apple to devise technologies that require fewer or different metal parts. Such regulatory uncertainty could dissuade investors from backing new mining approaches that might never be used.

The biologist Van Dover said she doesn’t outright oppose the Mining Code; rather, rules should include stringent stipulations, such as requirements to monitor environmental impacts and immediately stop operations once damage is detected. “I don’t see why the code couldn’t be so well-written that it would not allow the ISA to make a mistake,” she said.

A battle is brewing over the fate of the deep ocean. Huge swaths of seafloor are rich in metals—nickel, copper, cobalt, zinc—that are key to making electric vehicle batteries, solar panels, and smartphones. Mining companies have proposed scraping and vacuuming the dark expanse to provide supplies for metal-intensive technologies. Marine scientists and environmentalists oppose such plans, warning of huge and potentially permanent damage to fragile ecosystems.

Pietro Filardo is among the technology developers who are working to find common ground.

Image: Pliant Energy Systems

His company, Pliant Energy Systems, has built what looks like a black mechanical stingray. Its soft, rippling fins use hyperbolic geometry to move in a traveling wave pattern, propelling the skateboard-sized device through water. From an airy waterfront lab in Brooklyn, New York, Filardo’s team is developing tools and algorithms to transform the robot into an autonomous device equipped with grippers. Their goal is to pluck polymetallic nodules—potato-sized deposits of precious ores—off the seafloor without disrupting precious habitats.

“On the one hand, we need these metals to electrify and decarbonize. On the other hand, people worry we’re going to destroy deep ocean ecosystems that we know very little about,” Filardo said. He described deep sea mining as the “killer app” for Pliant’s robot—a potentially lucrative use for the startup’s minimally invasive design.

How deep seas will be mined, and where, is ultimately up to the International Seabed Authority (ISA), a group of 168 member countries. In October, the intergovernmental body is expected to adopt a sweeping set of technical and environmental standards, known as the Mining Code, that could pave the way for private companies to access large tracts of seafloor. 

The ISA has already awarded 30 exploratory permits to contractors in sections of the Atlantic, Pacific, and Indian Oceans. Over half the permits are for prospecting polymetallic nodules, primarily in the Clarion-Clipperton Zone, a hotspot south of Hawaii and west of Mexico.

Researchers have tested nodule mining technology since the 1970s, mainly in national waters. Existing approaches include sweeping the seafloor with hydraulic suction dredges to pump up sediment, filter out minerals, and dump the resulting slurry in the ocean or tailing ponds. In India, the National Institute of Ocean Technology is building a tracked “crawler” vehicle with a large scoop to collect, crush, and pump nodules up to a mother ship.

Mining proponents say such techniques are better for people and the environment than dangerous, exploitative land-based mining practices. Yet ocean experts warn that stirring up sediment and displacing organisms that live on nodules could destroy deep sea habitats that took millions of years to develop. 

“One thing I often talk about is, ‘How do we fix it if we break it? How are we going to know we broke it?’” said Cindy Lee Van Dover, a deep sea biologist and professor at Duke University’s Nicholas School of the Environment. She said much more research is required to understand the potential effects on ocean ecosystems, which foster fisheries, absorb carbon dioxide, and produce most of the Earth’s oxygen.

Significant work is also needed to transform robots into metal collectors that can operate some 6,000 meters below the ocean surface.

Photo: Pliant Energy Systems Former Pliant engineers Daniel Zimmerman (right) and Michael Weaker work on a prototype that harnesses energy from rivers and streams—the precursor to Velox.

Pliant’s first prototype, called Velox, can navigate the depths of a swimming pool and the shallow ocean “surf zone” where waves crash into the sand. Inside Velox, an onboard CPU distributes power to actuators that drive the undulating motions in the flexible fins. Unlike a propeller thruster, which uses a rapidly rotating blade to move small jets of water at high velocity, Pliant’s undulating fins move large volumes of water at low velocity. By using the water’s large surface area, the robot can make rapid local maneuvers using relatively little battery input, allowing the device to operate for longer periods before needing to recharge, Filardo said. 

The design also stirs up less sediment on the seafloor, a potential advantage in sensitive deep sea environments, he added.

The Brooklyn company is partnering with the Massachusetts Institute of Technology to develop a larger next-generation robot, called C-Ray. The highly maneuverable device will twist and roll like a sea otter. Using metal detectors and a mix of camera hardware and computer algorithms, C-Ray will likely be used to surveil the surf zone for potential hazards to the U.S. Navy, who is sponsoring the research program.

Illustration: Pliant Energy Systems A conceptual illustration of C-Ray robots collecting deep sea polymetallic nodules.

The partners ultimately aim to deploy “swarms” of autonomous C-Rays that communicate via a “hive mind”—applications that would also serve to mine polymetallic nodules. Pliant envisions launching hundreds of gripper-equipped robots that roam the seafloor and place nodules in cages that float to the surface on gas-filled lift bags. Filardo suggested that C-Ray could also swap nodules with lower-value stones, allowing organisms to regrow on the seafloor.

A separate project in Italy may also yield new tools for plucking the metal-rich orbs.

SILVER2 is a six-legged robot that can feel its way around the dark and turbid seafloor, without the aid of cameras or lasers, by pushing its legs in repeated, frequent cycles.

“We started by looking at what crabs did underwater,” said Marcello Calisti, an assistant professor at the BioRobotics Institute, in the Sant'Anna School of Advanced Studies. He likened the movements to people walking waist-deep in water and using the sand as leverage, or the “punter” on a flat-bottomed river boat who uses a long wooden pole to propel the vessel forward.

Photo: BioRobotics Institute/Sant'Anna School of Advanced Studies

Calisti and colleagues spent most of July at a seaside lab in Livorno, Italy, testing the 20-kilogram prototype in shallow water. SILVER2 is equipped with a soft elastic gripper that gently envelopes objects, as if cupping them in the palm of a hand. Researchers used the crab-like robot to collect plastic litter on the seabed and deposit the debris in a central collection bin.

Although SILVER2 isn’t intended for deep sea mining, Calisti said he could foresee potential applications in the sector if his team can scale the technology.

For developers like Pliant, their ability to raise funding and achieve their mining robots will largely depend on the International Seabed Authority’s next meeting. Opponents of ocean mining are pushing to pause discussions on the Mining Code to give scientists more time to evaluate risks, and to allow companies like Tesla or Apple to devise technologies that require fewer or different metal parts. Such regulatory uncertainty could dissuade investors from backing new mining approaches that might never be used.

The biologist Van Dover said she doesn’t outright oppose the Mining Code; rather, rules should include stringent stipulations, such as requirements to monitor environmental impacts and immediately stop operations once damage is detected. “I don’t see why the code couldn’t be so well-written that it would not allow the ISA to make a mistake,” she said.

IBM must be brimming with confidence about its new automated system for performing chemical synthesis because Big Blue just had twenty or so journalists demo the complex technology live in a virtual room.

IBM even had one of the journalists choose the molecule for the demo: a molecule in a potential Covid-19 treatment. And then we watched as the system synthesized and tested the molecule and provided its analysis in a PDF document that we all saw in the other journalist’s computer. It all worked; again, that’s confidence.

The complex system is based upon technology IBM started developing three years ago that uses artificial intelligence (AI) to predict chemical reactions. In August 2018, IBM made this service available via the Cloud and dubbed it RXN for Chemistry.

Now, the company has added a new wrinkle to its Cloud-based AI: robotics. This new and improved system is no longer named simply RXN for Chemistry, but RoboRXN for Chemistry.

All of the journalists assembled for this live demo of RoboRXN could watch as the robotic system executed various steps, such as moving the reactor to a small reagent and then moving the solvent to a small reagent. The robotic system carried out the entire set of procedures—completing the synthesis and analysis of the molecule—in eight steps.

Image: IBM Research IBM RXN helps predict chemical reaction outcomes or design retrosynthesis in seconds.

In regular practice, a user will be able to suggest a combination of molecules they would like to test. The AI will pick up the order and task a robotic system to run the reactions necessary to produce and test the molecule. Users will be provided analyses of how well their molecules performed.

Back in March of this year, Silicon Valley-based startup Strateos demonstrated something similar that they had developed. That system also employed a robotic system to help researchers working from the Cloud create new chemical compounds. However, what distinguishes IBM’s system is its incorporation of a third element: the AI.

The backbone of IBM’s AI model is a machine learning translation method that treats chemistry like language translation. It translates the language of chemistry by converting reactants and reagents to products through the use of Statistical Machine Intelligence and Learning Engine (SMILE) representation to describe chemical entities.

IBM has also leveraged an automatic data driven strategy to ensure the quality of its data. Researchers there used millions of chemical reactions to teach the AI system chemistry, but contained within that data set were errors. So, how did IBM clean this so-called noisy data to eliminate the potential for bad models?

According to Alessandra Toniato, a researcher at IBM Zurichh, the team implemented what they dubbed the “forgetting experiment.”

Toniato explains that, in this approach, they asked the AI model how sure it was that the chemical examples it was given were examples of correct chemistry. When faced with this choice, the AI identified chemistry that it had “never learnt,” “forgotten six times,” or “never forgotten.” Those that were “never forgotten” were examples that were clean, and in this way they were able to clean the data that AI had been presented.

While the AI has always been part of the RXN for Chemistry, the robotics is the newest element. The main benefit that turning over the carrying out of the reactions to a robotic system is expected to yield is to free up chemists from doing the often tedious process of having to design a synthesis from scratch, says Matteo Manica, a research staff member in Cognitive Health Care and Life Sciences at IBM Research Zürich.

“In this demo, you could see how the system is synergistic between a human and AI,” said Manica. “Combine that with the fact that we can run all these processes with a robotic system 24/7 from anywhere in the world, and you can see how it will really help up to speed up the whole process.”

There appear to be two business models that IBM is pursuing with its latest technology. One is to deploy the entire system on the premises of a company. The other is to offer licenses to private Cloud installations.

Photo: Michael Buholzer Teodoro Laino of IBM Research Europe.

“From a business perspective you can think of having a system like we demonstrated being replicated on the premise within companies or research groups that would like to have the technology available at their disposal,” says Teodoro Laino, distinguished RSM, manager at IBM Research Europe.  “On the other hand, we are also pushing at bringing the entire system to a service level.”

Just as IBM is brimming with confidence about its new technology, the company also has grand aspirations for it.

Laino adds: “Our aim is to provide chemical services across the world, a sort of Amazon of chemistry, where instead of looking for chemistry already in stock, you are asking for chemistry on demand.”

Back to IEEE COVID-19 Resources

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

CLAWAR 2020 – August 24-26, 2020 – [Online Conference] ICUAS 2020 – September 1-4, 2020 – Athens, Greece ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Online Conference] IROS 2020 – October 25-29, 2020 – Las Vegas, Nev., USA CYBATHLON 2020 – November 13-14, 2020 – [Online Event] ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA

Let us know if you have suggestions for next week, and enjoy today's videos.

Tokyo startup Telexistence has recently unveiled a new robot called the Model-T, an advanced teleoperated humanoid that can use tools and grasp a wide range of objects. Japanese convenience store chain FamilyMart plans to test the Model-T to restock shelves in up to 20 stores by 2022. In the trial, a human “pilot” will operate the robot remotely, handling items like beverage bottles, rice balls, sandwiches, and bento boxes.

With Model-T and AWP, FamilyMart and TX aim to realize a completely new store operation by remoteizing and automating the merchandise restocking work, which requires a large number of labor-hours. As a result, stores can operate with less number of workers and enable them to recruit employees regardless of the store’s physical location.

[ Telexistence ]

Quadruped dance-off should be a new robotics competition at IROS or ICRA.

I dunno though, that moonwalk might keep Spot in the lead...

[ Unitree ]

Through a hybrid of simulation and real-life training, this air muscle robot is learning to play table tennis.

Table tennis requires to execute fast and precise motions. To gain precision it is necessary to explore in this high-speed regimes, however, exploration can be safety-critical at the same time. The combination of RL and muscular soft robots allows to close this gap. While robots actuated by pneumatic artificial muscles generate high forces that are required for e.g. smashing, they also offer safe execution of explosive motions due to antagonistic actuation.

To enable practical training without real balls, we introduce Hybrid Sim and Real Training (HYSR) that replays prerecorded real balls in simulation while executing actions on the real system. In this manner, RL can learn the challenging motor control of the PAM-driven robot while executing ~15000 hitting motions.

[ Max Planck Institute ]

Thanks Dieter!

Anthony Cowley wrote in to share his recent thesis work on UPSLAM, a fast and lightweight SLAM technique that records data in panoramic depth images (just PNGs) that are easy to visualize and even easier to share between robots, even on low-bandwidth networks.

[ UPenn ]

Thanks Anthony!

GITAI’s G1 is the space dedicated general-purpose robot. G1 robot will enable automation of various tasks internally & externally on space stations and for lunar base development.

[ Gitai ]

The University of Michigan has a fancy new treadmill that’s built right into the floor, which proves to be a bit much for Mini Cheetah.

But Cassie Blue won’t get stuck on no treadmill! She goes for a 0.3 mile walk across campus, which ends when a certain someone ran the gantry into Cassie Blue’s foot.

[ Michigan Robotics ]

Some serious quadruped research going on at UT Austin Human Centered Robotics Lab.

[ HCRL ]

Will Burrard-Lucas has spent lockdown upgrading his slightly indestructible BeetleCam wildlife photographing robot.

[ Will Burrard-Lucas ]

Teleoperated surgical robots are becoming commonplace in operating rooms, but many are massive (sometimes taking up an entire room) and are difficult to manipulate, especially if a complication arises and the robot needs to removed from the patient. A new collaboration between the Wyss Institute, Harvard University, and Sony Corporation has created the mini-RCM, a surgical robot the size of a tennis ball that weighs as much as a penny, and performed significantly better than manually operated tools in delicate mock-surgical procedures. Importantly, its small size means it is more comparable to the human tissues and structures on which it operates, and it can easily be removed by hand if needed.

[ Harvard Wyss ]

Yaskawa appears to be working on a robot that can scan you with a temperature gun and then jam a mask on your face?

[ Motoman ]

Maybe we should just not have people working in mines anymore, how about that?

[ Exyn ]

Many current human-robot interactive systems tend to use accurate and fast – but also costly – actuators and tracking systems to establish working prototypes that are safe to use and deploy for user studies. This paper presents an embedded framework to build a desktop space for human-robot interaction, using an open-source robot arm, as well as two RGB cameras connected to a Raspberry Pi-based controller that allow a fast yet low-cost object tracking and manipulation in 3D. We show in our evaluations that this facilitates prototyping a number of systems in which user and robot arm can commonly interact with physical objects.

[ Paper ]

IBM Research is proud to host professor Yoshua Bengio — one of the world’s leading experts in AI — in a discussion of how AI can contribute to the fight against COVID-19.

[ IBM Research ]

Ira Pastor, ideaXme life sciences ambassador interviews Professor Dr. Hiroshi Ishiguro, the Director of the Intelligent Robotics Laboratory, of the Department of Systems Innovation, in the Graduate School of Engineering Science, at Osaka University, Japan.

[ ideaXme ]

A CVPR talk from Stanford’s Chelsea Finn on “Generalization in Visuomotor Learning.”

[ Stanford ]

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

CLAWAR 2020 – August 24-26, 2020 – [Online Conference] ICUAS 2020 – September 1-4, 2020 – Athens, Greece ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Online Conference] IROS 2020 – October 25-29, 2020 – Las Vegas, Nev., USA CYBATHLON 2020 – November 13-14, 2020 – [Online Event] ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA

Let us know if you have suggestions for next week, and enjoy today's videos.

Tokyo startup Telexistence has recently unveiled a new robot called the Model-T, an advanced teleoperated humanoid that can use tools and grasp a wide range of objects. Japanese convenience store chain FamilyMart plans to test the Model-T to restock shelves in up to 20 stores by 2022. In the trial, a human “pilot” will operate the robot remotely, handling items like beverage bottles, rice balls, sandwiches, and bento boxes.

With Model-T and AWP, FamilyMart and TX aim to realize a completely new store operation by remoteizing and automating the merchandise restocking work, which requires a large number of labor-hours. As a result, stores can operate with less number of workers and enable them to recruit employees regardless of the store’s physical location.

[ Telexistence ]

Quadruped dance-off should be a new robotics competition at IROS or ICRA.

I dunno though, that moonwalk might keep Spot in the lead...

[ Unitree ]

Through a hybrid of simulation and real-life training, this air muscle robot is learning to play table tennis.

Table tennis requires to execute fast and precise motions. To gain precision it is necessary to explore in this high-speed regimes, however, exploration can be safety-critical at the same time. The combination of RL and muscular soft robots allows to close this gap. While robots actuated by pneumatic artificial muscles generate high forces that are required for e.g. smashing, they also offer safe execution of explosive motions due to antagonistic actuation.

To enable practical training without real balls, we introduce Hybrid Sim and Real Training (HYSR) that replays prerecorded real balls in simulation while executing actions on the real system. In this manner, RL can learn the challenging motor control of the PAM-driven robot while executing ~15000 hitting motions.

[ Max Planck Institute ]

Thanks Dieter!

Anthony Cowley wrote in to share his recent thesis work on UPSLAM, a fast and lightweight SLAM technique that records data in panoramic depth images (just PNGs) that are easy to visualize and even easier to share between robots, even on low-bandwidth networks.

[ UPenn ]

Thanks Anthony!

GITAI’s G1 is the space dedicated general-purpose robot. G1 robot will enable automation of various tasks internally & externally on space stations and for lunar base development.

[ Gitai ]

The University of Michigan has a fancy new treadmill that’s built right into the floor, which proves to be a bit much for Mini Cheetah.

But Cassie Blue won’t get stuck on no treadmill! She goes for a 0.3 mile walk across campus, which ends when a certain someone ran the gantry into Cassie Blue’s foot.

[ Michigan Robotics ]

Some serious quadruped research going on at UT Austin Human Centered Robotics Lab.

[ HCRL ]

Will Burrard-Lucas has spent lockdown upgrading his slightly indestructible BeetleCam wildlife photographing robot.

[ Will Burrard-Lucas ]

Teleoperated surgical robots are becoming commonplace in operating rooms, but many are massive (sometimes taking up an entire room) and are difficult to manipulate, especially if a complication arises and the robot needs to removed from the patient. A new collaboration between the Wyss Institute, Harvard University, and Sony Corporation has created the mini-RCM, a surgical robot the size of a tennis ball that weighs as much as a penny, and performed significantly better than manually operated tools in delicate mock-surgical procedures. Importantly, its small size means it is more comparable to the human tissues and structures on which it operates, and it can easily be removed by hand if needed.

[ Harvard Wyss ]

Yaskawa appears to be working on a robot that can scan you with a temperature gun and then jam a mask on your face?

[ Motoman ]

Maybe we should just not have people working in mines anymore, how about that?

[ Exyn ]

Many current human-robot interactive systems tend to use accurate and fast – but also costly – actuators and tracking systems to establish working prototypes that are safe to use and deploy for user studies. This paper presents an embedded framework to build a desktop space for human-robot interaction, using an open-source robot arm, as well as two RGB cameras connected to a Raspberry Pi-based controller that allow a fast yet low-cost object tracking and manipulation in 3D. We show in our evaluations that this facilitates prototyping a number of systems in which user and robot arm can commonly interact with physical objects.

[ Paper ]

IBM Research is proud to host professor Yoshua Bengio — one of the world’s leading experts in AI — in a discussion of how AI can contribute to the fight against COVID-19.

[ IBM Research ]

Ira Pastor, ideaXme life sciences ambassador interviews Professor Dr. Hiroshi Ishiguro, the Director of the Intelligent Robotics Laboratory, of the Department of Systems Innovation, in the Graduate School of Engineering Science, at Osaka University, Japan.

[ ideaXme ]

A CVPR talk from Stanford’s Chelsea Finn on “Generalization in Visuomotor Learning.”

[ Stanford ]

Computer Tomography (CT) is an imaging procedure that combines many X-ray measurements taken from different angles. The segmentation of areas in the CT images provides a valuable aid to physicians and radiologists in order to better provide a patient diagnose. The CT scans of a body torso usually include different neighboring internal body organs. Deep learning has become the state-of-the-art in medical image segmentation. For such techniques, in order to perform a successful segmentation, it is of great importance that the network learns to focus on the organ of interest and surrounding structures and also that the network can detect target regions of different sizes. In this paper, we propose the extension of a popular deep learning methodology, Convolutional Neural Networks (CNN), by including deep supervision and attention gates. Our experimental evaluation shows that the inclusion of attention and deep supervision results in consistent improvement of the tumor prediction accuracy across the different datasets and training sizes while adding minimal computational overhead.

Muscle models and animal observations suggest that physical damping is beneficial for stabilization. Still, only a few implementations of physical damping exist in compliant robotic legged locomotion. It remains unclear how physical damping can be exploited for locomotion tasks, while its advantages as sensor-free, adaptive force- and negative work-producing actuators are promising. In a simplified numerical leg model, we studied the energy dissipation from viscous and Coulomb damping during vertical drops with ground-level perturbations. A parallel spring- damper is engaged between touch-down and mid-stance, and its damper auto-decouples from mid-stance to takeoff. Our simulations indicate that an adjustable and viscous damper is desired. In hardware we explored effective viscous damping and adjustability, and quantified the dissipated energy. We tested two mechanical, leg-mounted damping mechanisms: a commercial hydraulic damper, and a custom-made pneumatic damper. The pneumatic damper exploits a rolling diaphragm with an adjustable orifice, minimizing Coulomb damping effects while permitting adjustable resistance. Experimental results show that the leg-mounted, hydraulic damper exhibits the most effective viscous damping. Adjusting the orifice setting did not result in substantial changes of dissipated energy per drop, unlike adjusting the damping parameters in the numerical model. Consequently, we also emphasize the importance of characterizing physical dampers during real legged impacts to evaluate their effectiveness for compliant legged locomotion.

At IROS last year, Caltech and NASA’s Jet Propulsion Lab presented a prototype for a ballistically launched quadrotor—once folded up into a sort of football shape with fins, the drone is stuffed into a tube and then fired straight up with a blast of compressed CO2, at which point it unfolds itself, stabilizes, and then flies off. It’s been about half a year, and the prototype has been scaled up in both size and capability, now with a half-dozen rotors and full onboard autonomy that can (barely) squeeze into a 6-inch tube.

SQUID stands for Streamlined Quick Unfolding Investigation Drone. The original 3-inch (7.6-centimeter) SQUID that we wrote about last year has been demoted to “micro-SQUID,” and the new SQUID is this much beefier 6-inch version. You should read our earlier article on micro-SQUID for some background on this concept, but generally, tube-launched drones are unique in that they remove the requirement for the kind of specific takeoff conditions that most drones expect—stationary and on the ground and not close to anything that objects to being sliced to bits. A demonstration last year showed micro-SQUID launching from a moving vehicle, but the overall idea is that you can launch a SQUID instantly and from pretty much anywhere.

The point of micro-SQUID was to work out the general aerodynamic and structural principles for a ballistically launched multirotor, rather than to develop something mission capable. Mission capable means, among other things, onboard autonomy without reliance on GPS, which in turn calls for sensing and computing that’s heavy and power hungry enough that the entire vehicle needed to be scaled up. The new 6-inch SQUID features some major updates, including an aerodynamic redesign for improved passive stabilization during launch and ballistic flight through the use of deployable fins. The autonomy hardware consists of a camera (FLIR Chameleon3), rangefinder (TeraRanger Evo 60m), IMU/barometer (VectorNav VN-100), and onboard computer (NVIDIA Jetson TX2).

Image: Caltech & NASA JPL Top: SQUID overview. Bottom: SQUID partially inside the launcher tube (a), with its arms and fins fully deployed from a side (b), and top perspective (c).

The structural and aerodynamic changes are necessary because SQUID spends the first phase of its flight not really flying at all, but rather just following the ballistic trajectory that it’s on once it leaves the launcher. If it’s just going straight up, that’s not too bad, but things start to get more complicated if the drone gets launched at an angle, or from a moving vehicle. Having a high center of mass helps (the battery lives in the nose cone), and deployable fins pull double duty by keeping the drone passively pointed into the airstream while also serving as landing gear—without the fins, it would start to tumble after leaving the tube, and then good luck trying to control it. In order for the fins to be both foldable and stable enough for SQUID to land on, they’ve got a latching mechanism that helps keep them rigid, and apparently once everything got put together it took a little bit of sanding of the arm hinges before the drone would actually fit into the launch tube.

That 6-inch hard stop on the diameter of SQUID turned out to be a real challenge. Most drones are power or mass constrained, but SQUID is instead volume constrained. Not only do you have to cram all of your batteries and computers into that space, you have to make sure that the sensors have the field of view that they need while keeping in mind that in its folded state all the arms and legs have to share the same space as everything else. It turns out that SQUID is very well optimized, though, weighing just 3.3 kilograms, only about 0.3 kg more than what the roboticists estimate a nonfoldable, nonoptimized conventional drone with similar capabilities would weigh. 

So why bother with all of this hassle for the whole tube launch thing? There are a bunch of reasons that make it worth the effort: 

  • It’s fast to launch. There’s no unpacking or setting up or finding a flat spot or telling everyone to stand back, just push a button and bam, SQUID is out of the tube at 12 meters per second and in flight. 
  • It’s safe to launch. Unless someone is sitting directly on top of the launch tube (in which case you could argue that they deserve what they’re about to experience), the launch rapidly clears human level before deploying any dangerously spinny bits. 
  • It can launch while moving. This is a big one—the ballistic launch and self-stabilization means that SQUID can be reliably launched from a moving vehicle moving at up to 80 kilometers per hour, like a truck or a boat, significantly increasing its utility, especially in emergency scenarios.
  • It can sometimes launch through things. The researchers point out that in its most aerodynamic shape (without fins or rotors deployed), SQUID could potentially be launched straight through tree canopies or power lines if necessary, which is a totally unique capability for a rotorcraft.

We asked the researchers about their experience developing the larger version of SQUID, and they shared this behind-the-scenes story with us about how they managed to set things up so that they didn’t crash even once:

Moving to a larger SQUID was hard technically (as we had to design an entirely new vehicle), but the testing logistics was a huge jump in difficulty. For our smaller SQUID, simply a net and some spare parts would suffice to keep testing going for a day. But when we moved to the bigger SQUID, we’re throwing something a lot heavier, and packed with expensive electronics for autonomy, into the sky. 

An indoor tether system was challenging to set up because the height of the CAST arena (42 foot-tall) meant the ideal locating point for the tether was completely inaccessible without a cherry-picker. The Caltech Drone Club stepped up, and helped construct the tether system by weaving a tiny quadrotor towing fishing line around the ceiling beams. The fishing line was then used to pull larger ropes through.

One of the interesting things that was learnt with the tether system was the extreme acceleration of SQUID as it exited the launch tube meant the tether cable becomes very slack and actually risks getting tangled with or cut by the propellers. Luckily our incremental testing campaign caught this before we had any incidents. To deal with this slack tether situation, we constructed a nose cone with a 5 foot carbon fiber tube mounted at the apex, which we called SQUID’s swordfish nose (we had a bit of an aquatic theme going already). A tether attached to SQUID’s frame runs through the tube and connects to the larger CAST tether system. We confirmed that during launch (for our given launch parameters), the tether never droops lower than the tube, so we prevented all tether-propeller interactions.

As you might expect from a drone from Caltech and JPL, long term the plan is to start thinking about aerial deployment—like, launching small drones from larger aircraft. This could eventually provide a way for small drones to be deployed from spacecraft on Mars during atmospheric entry, potentially reducing the need for a large lander. In fact, it’s common for aeroshells that deliver landers to planetary surfaces to rebalance themselves during atmospheric entry by dropping a bunch (like, 150 kg) of weight to adjust their angle of attack. Those weights are utterly useless chunks of tungsten, but if it was possible to drop some midair-deployable drones instead, you could potentially do a whole lot of extra science without adding extra mass or risk to an existing mission.

Design and Autonomous Stabilization of a Ballistically-Launched Multirotor,” by Amanda Bouman, Paul Nadan, Matthew Anderson, Daniel Pastor, Jacob Izraelevitz, Joel Burdick, and Brett Kennedy from Caltech and JPL, was presented at ICRA 2020, where it was awarded best paper in Unmanned Aerial Vehicles.

Back to IEEE Journal Watch

At IROS last year, Caltech and NASA’s Jet Propulsion Lab presented a prototype for a ballistically launched quadrotor—once folded up into a sort of football shape with fins, the drone is stuffed into a tube and then fired straight up with a blast of compressed CO2, at which point it unfolds itself, stabilizes, and then flies off. It’s been about half a year, and the prototype has been scaled up in both size and capability, now with a half-dozen rotors and full onboard autonomy that can (barely) squeeze into a 6-inch tube.

SQUID stands for Streamlined Quick Unfolding Investigation Drone. The original 3-inch (7.6-centimeter) SQUID that we wrote about last year has been demoted to “micro-SQUID,” and the new SQUID is this much beefier 6-inch version. You should read our earlier article on micro-SQUID for some background on this concept, but generally, tube-launched drones are unique in that they remove the requirement for the kind of specific takeoff conditions that most drones expect—stationary and on the ground and not close to anything that objects to being sliced to bits. A demonstration last year showed micro-SQUID launching from a moving vehicle, but the overall idea is that you can launch a SQUID instantly and from pretty much anywhere.

The point of micro-SQUID was to work out the general aerodynamic and structural principles for a ballistically launched multirotor, rather than to develop something mission capable. Mission capable means, among other things, onboard autonomy without reliance on GPS, which in turn calls for sensing and computing that’s heavy and power hungry enough that the entire vehicle needed to be scaled up. The new 6-inch SQUID features some major updates, including an aerodynamic redesign for improved passive stabilization during launch and ballistic flight through the use of deployable fins. The autonomy hardware consists of a camera (FLIR Chameleon3), rangefinder (TeraRanger Evo 60m), IMU/barometer (VectorNav VN-100), and onboard computer (NVIDIA Jetson TX2).

Image: Caltech & NASA JPL Top: SQUID overview. Bottom: SQUID partially inside the launcher tube (a), with its arms and fins fully deployed from a side (b), and top perspective (c).

The structural and aerodynamic changes are necessary because SQUID spends the first phase of its flight not really flying at all, but rather just following the ballistic trajectory that it’s on once it leaves the launcher. If it’s just going straight up, that’s not too bad, but things start to get more complicated if the drone gets launched at an angle, or from a moving vehicle. Having a high center of mass helps (the battery lives in the nose cone), and deployable fins pull double duty by keeping the drone passively pointed into the airstream while also serving as landing gear—without the fins, it would start to tumble after leaving the tube, and then good luck trying to control it. In order for the fins to be both foldable and stable enough for SQUID to land on, they’ve got a latching mechanism that helps keep them rigid, and apparently once everything got put together it took a little bit of sanding of the arm hinges before the drone would actually fit into the launch tube.

That 6-inch hard stop on the diameter of SQUID turned out to be a real challenge. Most drones are power or mass constrained, but SQUID is instead volume constrained. Not only do you have to cram all of your batteries and computers into that space, you have to make sure that the sensors have the field of view that they need while keeping in mind that in its folded state all the arms and legs have to share the same space as everything else. It turns out that SQUID is very well optimized, though, weighing just 3.3 kilograms, only about 0.3 kg more than what the roboticists estimate a nonfoldable, nonoptimized conventional drone with similar capabilities would weigh. 

So why bother with all of this hassle for the whole tube launch thing? There are a bunch of reasons that make it worth the effort: 

  • It’s fast to launch. There’s no unpacking or setting up or finding a flat spot or telling everyone to stand back, just push a button and bam, SQUID is out of the tube at 12 meters per second and in flight. 
  • It’s safe to launch. Unless someone is sitting directly on top of the launch tube (in which case you could argue that they deserve what they’re about to experience), the launch rapidly clears human level before deploying any dangerously spinny bits. 
  • It can launch while moving. This is a big one—the ballistic launch and self-stabilization means that SQUID can be reliably launched from a moving vehicle moving at up to 80 kilometers per hour, like a truck or a boat, significantly increasing its utility, especially in emergency scenarios.
  • It can sometimes launch through things. The researchers point out that in its most aerodynamic shape (without fins or rotors deployed), SQUID could potentially be launched straight through tree canopies or power lines if necessary, which is a totally unique capability for a rotorcraft.

We asked the researchers about their experience developing the larger version of SQUID, and they shared this behind-the-scenes story with us about how they managed to set things up so that they didn’t crash even once:

Moving to a larger SQUID was hard technically (as we had to design an entirely new vehicle), but the testing logistics was a huge jump in difficulty. For our smaller SQUID, simply a net and some spare parts would suffice to keep testing going for a day. But when we moved to the bigger SQUID, we’re throwing something a lot heavier, and packed with expensive electronics for autonomy, into the sky. 

An indoor tether system was challenging to set up because the height of the CAST arena (42 foot-tall) meant the ideal locating point for the tether was completely inaccessible without a cherry-picker. The Caltech Drone Club stepped up, and helped construct the tether system by weaving a tiny quadrotor towing fishing line around the ceiling beams. The fishing line was then used to pull larger ropes through.

One of the interesting things that was learnt with the tether system was the extreme acceleration of SQUID as it exited the launch tube meant the tether cable becomes very slack and actually risks getting tangled with or cut by the propellers. Luckily our incremental testing campaign caught this before we had any incidents. To deal with this slack tether situation, we constructed a nose cone with a 5 foot carbon fiber tube mounted at the apex, which we called SQUID’s swordfish nose (we had a bit of an aquatic theme going already). A tether attached to SQUID’s frame runs through the tube and connects to the larger CAST tether system. We confirmed that during launch (for our given launch parameters), the tether never droops lower than the tube, so we prevented all tether-propeller interactions.

As you might expect from a drone from Caltech and JPL, long term the plan is to start thinking about aerial deployment—like, launching small drones from larger aircraft. This could eventually provide a way for small drones to be deployed from spacecraft on Mars during atmospheric entry, potentially reducing the need for a large lander. In fact, it’s common for aeroshells that deliver landers to planetary surfaces to rebalance themselves during atmospheric entry by dropping a bunch (like, 150 kg) of weight to adjust their angle of attack. Those weights are utterly useless chunks of tungsten, but if it was possible to drop some midair-deployable drones instead, you could potentially do a whole lot of extra science without adding extra mass or risk to an existing mission.

Design and Autonomous Stabilization of a Ballistically-Launched Multirotor,” by Amanda Bouman, Paul Nadan, Matthew Anderson, Daniel Pastor, Jacob Izraelevitz, Joel Burdick, and Brett Kennedy from Caltech and JPL, was presented at ICRA 2020, where it was awarded best paper in Unmanned Aerial Vehicles.

Back to IEEE Journal Watch

Since the release of the very first Roomba in 2002, iRobot’s long-term goal has been to deliver cleaner floors in a way that’s effortless and invisible. Which sounds pretty great, right? And arguably, iRobot has managed to do exactly this, with its most recent generation of robot vacuums that make their own maps and empty their own dustbins. For those of us who trust our robots, this is awesome, but iRobot has gradually been realizing that many Roomba users either don’t want this level of autonomy, or aren’t ready for it.

Today, iRobot is announcing a major new update to its app that represents a significant shift of its overall approach to home robot autonomy. Humans are being brought back into the loop through software that tries to learn when, where, and how you clean so that your Roomba can adapt itself to your life rather than the other way around.

To understand why this is such a shift for iRobot, let’s take a very brief look back at how the Roomba interface has evolved over the last couple of decades. The first generation of Roomba had three buttons on it that allowed (or required) the user to select whether the room being vacuumed was small or medium or large in size. iRobot ditched that system one generation later, replacing the room size buttons with one single “clean” button. Programmable scheduling meant that users no longer needed to push any buttons at all, and with Roombas able to find their way back to their docking stations, all you needed to do was empty the dustbin. And with the most recent few generations (the S and i series), the dustbin emptying is also done for you, reducing direct interaction with the robot to once a month or less.

Image: iRobot iRobot CEO Colin Angle believes that working toward more intelligent human-robot collaboration is “the brave new frontier” of AI. “This whole journey has been earning the right to take this next step, because a robot can’t be responsive if it’s incompetent,” he says. “But thinking that autonomy was the destination was where I was just completely wrong.” 

The point that the top-end Roombas are at now reflects a goal that iRobot has been working toward since 2002: With autonomy, scheduling, and the clean base to empty the bin, you can set up your Roomba to vacuum when you’re not home, giving you cleaner floors every single day without you even being aware that the Roomba is hard at work while you’re out. It’s not just hands-off, it’s brain-off. No noise, no fuss, just things being cleaner thanks to the efforts of a robot that does its best to be invisible to you. Personally, I’ve been completely sold on this idea for home robots, and iRobot CEO Colin Angle was as well.

“I probably told you that the perfect Roomba is the Roomba that you never see, you never touch, you just come home everyday and it’s done the right thing,” Angle told us. “But customers don’t want that—they want to be able to control what the robot does. We started to hear this a couple years ago, and it took a while before it sunk in, but it made sense.”

How? Angle compares it to having a human come into your house to clean, but you weren’t allowed to tell them where or when to do their job. Maybe after a while, you’ll build up the amount of trust necessary for that to work, but in the short term, it would likely be frustrating. And people get frustrated with their Roombas for this reason. “The desire to have more control over what the robot does kept coming up, and for me, it required a pretty big shift in my view of what intelligence we were trying to build. Autonomy is not intelligence. We need to do something more.”

That something more, Angle says, is a partnership as opposed to autonomy. It’s an acknowledgement that not everyone has the same level of trust in robots as the people who build them. It’s an understanding that people want to have a feeling of control over their homes, that they have set up the way that they want, and that they’ve been cleaning the way that they want, and a robot shouldn’t just come in and do its own thing. 

This change in direction also represents a substantial shift in resources for iRobot, and the company has pivoted two-thirds of its engineering organization to focus on software-based collaborative intelligence rather than hardware.

“Until the robot proves that it knows enough about your home and about the way that you want your home cleaned,” Angle says, “you can’t move forward.” He adds that this is one of those things that seem obvious in retrospect, but even if they’d wanted to address the issue before, they didn’t have the technology to solve the problem. Now they do. “This whole journey has been earning the right to take this next step, because a robot can’t be responsive if it’s incompetent,” Angle says. “But thinking that autonomy was the destination was where I was just completely wrong.”

The previous iteration of the iRobot app (and Roombas themselves) are built around one big fat CLEAN button. The new approach instead tries to figure out in much more detail where the robot should clean, and when, using a mixture of autonomous technology and interaction with the user.

Where to Clean

Knowing where to clean depends on your Roomba having a detailed and accurate map of its environment. For several generations now, Roombas have been using visual mapping and localization (VSLAM) to build persistent maps of your home. These maps have been used to tell the Roomba to clean in specific rooms, but that’s about it. With the new update, Roombas with cameras will be able to recognize some objects and features in your home, including chairs, tables, couches, and even countertops. The robots will use these features to identify where messes tend to happen so that they can focus on those areas—like around the dining room table or along the front of the couch. 

We should take a minute here to clarify how the Roomba is using its camera. The original (primary?) purpose of the camera was for VSLAM, where the robot would take photos of your home, downsample them into QR-code-like patterns of light and dark, and then use those (with the assistance of other sensors) to navigate. Now the camera is also being used to take pictures of other stuff around your house to make that map more useful.

Photo: iRobot The robots will now try to fit into the kinds of cleaning routines that many people already have established. For example, the app may suggest an “after dinner” routine that cleans just around the kitchen and dining room table.

This is done through machine learning using a library of images of common household objects from a floor perspective that iRobot had to develop from scratch. Angle clarified for us that this is all done via a neural net that runs on the robot, and that “no recognizable images are ever stored on the robot or kept, and no images ever leave the robot.” Worst case, if all the data iRobot has about your home gets somehow stolen, the hacker would only know that (for example) your dining room has a table in it and the approximate size and location of that table, because the map iRobot has of your place only stores symbolic representations rather than images.

Another useful new feature is intended to help manage the “evil Roomba places” (as Angle puts it) that every home has that cause Roombas to get stuck. If the place is evil enough that Roomba has to call you for help because it gave up completely, Roomba will now remember, and suggest that either you make some changes or that it stops cleaning there, which seems reasonable.

When to Clean

It turns out that the primary cause of mission failure for Roombas is not that they get stuck or that they run out of battery—it’s user cancellation, usually because the robot is getting in the way or being noisy when you don’t want it to be. “If you kill a Roomba’s job because it annoys you,” points out Angle, “how is that robot being a good partner? I think it’s an epic fail.” Of course, it’s not the robot’s fault, because Roombas only clean when we tell them to, which Angle says is part of the problem. “People actually aren’t very good at making their own schedules—they tend to oversimplify, and not think through what their schedules are actually about, which leads to lots of [figurative] Roomba death.”

To help you figure out when the robot should actually be cleaning, the new app will look for patterns in when you ask the robot to clean, and then recommend a schedule based on those patterns. That might mean the robot cleans different areas at different times every day of the week. The app will also make scheduling recommendations that are event-based as well, integrated with other smart home devices. Would you prefer the Roomba to clean every time you leave the house? The app can integrate with your security system (or garage door, or any number of other things) and take care of that for you.

More generally, Roomba will now try to fit into the kinds of cleaning routines that many people already have established. For example, the app may suggest an “after dinner” routine that cleans just around the kitchen and dining room table. The app will also, to some extent, pay attention to the environment and season. It might suggest increasing your vacuuming frequency if pollen counts are especially high, or if it’s pet shedding season and you have a dog. Unfortunately, Roomba isn’t (yet?) capable of recognizing dogs on its own, so the app has to cheat a little bit by asking you some basic questions. 

A Smarter App Image: iRobot

The previous iteration of the iRobot app (and Roombas themselves) are built around one big fat CLEAN button. The new approach instead tries to figure out in much more detail where the robot should clean, and when, using a mixture of autonomous technology and interaction with the user.

The app update, which should be available starting today, is free. The scheduling and recommendations will work on every Roomba model, although for object recognition and anything related to mapping, you’ll need one of the more recent and fancier models with a camera. Future app updates will happen on a more aggressive schedule. Major app releases should happen every six months, with incremental updates happening even more frequently than that. 

Angle also told us that overall, this change in direction also represents a substantial shift in resources for iRobot, and the company has pivoted two-thirds of its engineering organization to focus on software-based collaborative intelligence rather than hardware. “It’s not like we’re done doing hardware,” Angle assured us. “But we do think about hardware differently. We view our robots as platforms that have longer life cycles, and each platform will be able to support multiple generations of software. We’ve kind of decoupled robot intelligence from hardware, and that’s a change.”

Angle believes that working toward more intelligent collaboration between humans and robots is “the brave new frontier of artificial intelligence. I expect it to be the frontier for a reasonable amount of time to come,” he adds. “We have a lot of work to do to create the type of easy-to-use experience that consumer robots need.”

Since the release of the very first Roomba in 2002, iRobot’s long-term goal has been to deliver cleaner floors in a way that’s effortless and invisible. Which sounds pretty great, right? And arguably, iRobot has managed to do exactly this, with its most recent generation of robot vacuums that make their own maps and empty their own dustbins. For those of us who trust our robots, this is awesome, but iRobot has gradually been realizing that many Roomba users either don’t want this level of autonomy, or aren’t ready for it.

Today, iRobot is announcing a major new update to its app that represents a significant shift of its overall approach to home robot autonomy. Humans are being brought back into the loop through software that tries to learn when, where, and how you clean so that your Roomba can adapt itself to your life rather than the other way around.

To understand why this is such a shift for iRobot, let’s take a very brief look back at how the Roomba interface has evolved over the last couple of decades. The first generation of Roomba had three buttons on it that allowed (or required) the user to select whether the room being vacuumed was small or medium or large in size. iRobot ditched that system one generation later, replacing the room size buttons with one single “clean” button. Programmable scheduling meant that users no longer needed to push any buttons at all, and with Roombas able to find their way back to their docking stations, all you needed to do was empty the dustbin. And with the most recent few generations (the S and i series), the dustbin emptying is also done for you, reducing direct interaction with the robot to once a month or less.

Image: iRobot iRobot CEO Colin Angle believes that working toward more intelligent human-robot collaboration is “the brave new frontier” of AI. “This whole journey has been earning the right to take this next step, because a robot can’t be responsive if it’s incompetent,” he says. “But thinking that autonomy was the destination was where I was just completely wrong.” 

The point that the top-end Roombas are at now reflects a goal that iRobot has been working toward since 2002: With autonomy, scheduling, and the clean base to empty the bin, you can set up your Roomba to vacuum when you’re not home, giving you cleaner floors every single day without you even being aware that the Roomba is hard at work while you’re out. It’s not just hands-off, it’s brain-off. No noise, no fuss, just things being cleaner thanks to the efforts of a robot that does its best to be invisible to you. Personally, I’ve been completely sold on this idea for home robots, and iRobot CEO Colin Angle was as well.

“I probably told you that the perfect Roomba is the Roomba that you never see, you never touch, you just come home everyday and it’s done the right thing,” Angle told us. “But customers don’t want that—they want to be able to control what the robot does. We started to hear this a couple years ago, and it took a while before it sunk in, but it made sense.”

How? Angle compares it to having a human come into your house to clean, but you weren’t allowed to tell them where or when to do their job. Maybe after a while, you’ll build up the amount of trust necessary for that to work, but in the short term, it would likely be frustrating. And people get frustrated with their Roombas for this reason. “The desire to have more control over what the robot does kept coming up, and for me, it required a pretty big shift in my view of what intelligence we were trying to build. Autonomy is not intelligence. We need to do something more.”

That something more, Angle says, is a partnership as opposed to autonomy. It’s an acknowledgement that not everyone has the same level of trust in robots as the people who build them. It’s an understanding that people want to have a feeling of control over their homes, that they have set up the way that they want, and that they’ve been cleaning the way that they want, and a robot shouldn’t just come in and do its own thing. 

This change in direction also represents a substantial shift in resources for iRobot, and the company has pivoted two-thirds of its engineering organization to focus on software-based collaborative intelligence rather than hardware.

“Until the robot proves that it knows enough about your home and about the way that you want your home cleaned,” Angle says, “you can’t move forward.” He adds that this is one of those things that seem obvious in retrospect, but even if they’d wanted to address the issue before, they didn’t have the technology to solve the problem. Now they do. “This whole journey has been earning the right to take this next step, because a robot can’t be responsive if it’s incompetent,” Angle says. “But thinking that autonomy was the destination was where I was just completely wrong.”

The previous iteration of the iRobot app (and Roombas themselves) are built around one big fat CLEAN button. The new approach instead tries to figure out in much more detail where the robot should clean, and when, using a mixture of autonomous technology and interaction with the user.

Where to Clean

Knowing where to clean depends on your Roomba having a detailed and accurate map of its environment. For several generations now, Roombas have been using visual mapping and localization (VSLAM) to build persistent maps of your home. These maps have been used to tell the Roomba to clean in specific rooms, but that’s about it. With the new update, Roombas with cameras will be able to recognize some objects and features in your home, including chairs, tables, couches, and even countertops. The robots will use these features to identify where messes tend to happen so that they can focus on those areas—like around the dining room table or along the front of the couch. 

We should take a minute here to clarify how the Roomba is using its camera. The original (primary?) purpose of the camera was for VSLAM, where the robot would take photos of your home, downsample them into QR-code-like patterns of light and dark, and then use those (with the assistance of other sensors) to navigate. Now the camera is also being used to take pictures of other stuff around your house to make that map more useful.

Photo: iRobot The robots will now try to fit into the kinds of cleaning routines that many people already have established. For example, the app may suggest an “after dinner” routine that cleans just around the kitchen and dining room table.

This is done through machine learning using a library of images of common household objects from a floor perspective that iRobot had to develop from scratch. Angle clarified for us that this is all done via a neural net that runs on the robot, and that “no recognizable images are ever stored on the robot or kept, and no images ever leave the robot.” Worst case, if all the data iRobot has about your home gets somehow stolen, the hacker would only know that (for example) your dining room has a table in it and the approximate size and location of that table, because the map iRobot has of your place only stores symbolic representations rather than images.

Another useful new feature is intended to help manage the “evil Roomba places” (as Angle puts it) that every home has that cause Roombas to get stuck. If the place is evil enough that Roomba has to call you for help because it gave up completely, Roomba will now remember, and suggest that either you make some changes or that it stops cleaning there, which seems reasonable.

When to Clean

It turns out that the primary cause of mission failure for Roombas is not that they get stuck or that they run out of battery—it’s user cancellation, usually because the robot is getting in the way or being noisy when you don’t want it to be. “If you kill a Roomba’s job because it annoys you,” points out Angle, “how is that robot being a good partner? I think it’s an epic fail.” Of course, it’s not the robot’s fault, because Roombas only clean when we tell them to, which Angle says is part of the problem. “People actually aren’t very good at making their own schedules—they tend to oversimplify, and not think through what their schedules are actually about, which leads to lots of [figurative] Roomba death.”

To help you figure out when the robot should actually be cleaning, the new app will look for patterns in when you ask the robot to clean, and then recommend a schedule based on those patterns. That might mean the robot cleans different areas at different times every day of the week. The app will also make scheduling recommendations that are event-based as well, integrated with other smart home devices. Would you prefer the Roomba to clean every time you leave the house? The app can integrate with your security system (or garage door, or any number of other things) and take care of that for you.

More generally, Roomba will now try to fit into the kinds of cleaning routines that many people already have established. For example, the app may suggest an “after dinner” routine that cleans just around the kitchen and dining room table. The app will also, to some extent, pay attention to the environment and season. It might suggest increasing your vacuuming frequency if pollen counts are especially high, or if it’s pet shedding season and you have a dog. Unfortunately, Roomba isn’t (yet?) capable of recognizing dogs on its own, so the app has to cheat a little bit by asking you some basic questions. 

A Smarter App Image: iRobot

The previous iteration of the iRobot app (and Roombas themselves) are built around one big fat CLEAN button. The new approach instead tries to figure out in much more detail where the robot should clean, and when, using a mixture of autonomous technology and interaction with the user.

The app update, which should be available starting today, is free. The scheduling and recommendations will work on every Roomba model, although for object recognition and anything related to mapping, you’ll need one of the more recent and fancier models with a camera. Future app updates will happen on a more aggressive schedule. Major app releases should happen every six months, with incremental updates happening even more frequently than that. 

Angle also told us that overall, this change in direction also represents a substantial shift in resources for iRobot, and the company has pivoted two-thirds of its engineering organization to focus on software-based collaborative intelligence rather than hardware. “It’s not like we’re done doing hardware,” Angle assured us. “But we do think about hardware differently. We view our robots as platforms that have longer life cycles, and each platform will be able to support multiple generations of software. We’ve kind of decoupled robot intelligence from hardware, and that’s a change.”

Angle believes that working toward more intelligent collaboration between humans and robots is “the brave new frontier of artificial intelligence. I expect it to be the frontier for a reasonable amount of time to come,” he adds. “We have a lot of work to do to create the type of easy-to-use experience that consumer robots need.”

Applications in remote inspection and medicine have motivated the recent development of innovative thin, flexible-backboned robots. However, such robots often experience difficulties in maintaining their intended posture under gravitational and other external loadings. Thin-stemmed climbing plants face many of the same problems. One highly effective solution adopted by such plants features the use of tendrils and tendril-like structures, or the intertwining of several individual stems to form braid-like structures. In this paper, we present new plant-inspired robotic tendril-bearing and intertwining stem hardware and corresponding novel attachment strategies for thin continuum robots. These contributions to robotics are motivated by new insights into plant tendril and intertwining mechanics and behavior. The practical applications of the resulting GrowBots is discussed in the context of space exploration and mining operations.

Let’s talk about bowels! Most of us have them, most of us use them a lot, and like anything that gets used a lot, they eventually need to get checked out to help make sure that everything will keep working the way it should for as long as you need it to. Generally, this means a colonoscopy, and while there are other ways of investigating what’s going on in your gut, a camera on a flexible tube is still “the gold-standard method of diagnosis and intervention,” according to some robotics researchers who want to change that up a bit.

The University of Colorado’s Advanced Medical Technologies Lab has been working on a tank robot called Endoculus that’s able to actively drive itself through your intestines, rather than being shoved. The good news is that it’s very small, and the bad news is that it’s probably not as small as you’d like it to be.

The reason why a robot like Endoculus is necessary (or at least a good idea) is that trying to stuff a semi-rigid endoscopy tube into the semi-floppy tube that is your intestine doesn’t always go smoothly. Sometimes, the tip of the endoscopy tube can get stuck, and as more tube is fed in, it causes the intestine to distend, which best case is painful and worst case can cause serious internal injuries. One way of solving this is with swallowable camera pills, but those don’t help you with tasks like taking tissue samples. A self-propelled system like Endoculus could reduce risk while also making the procedure faster and cheaper.

Image: Advanced Medical Technologies Lab/University of Colorado The researchers say that while the width of Endoculus is larger than a traditional endoscope, the device would require “minimal distention during use” and would “not cause pain or harm to the patient.” Future versions of the robot, they add, will “yield a smaller footprint.”

Endoculus gets around with four sets of treads, angled to provide better traction against the curved walls of your gut. The treads are micropillared, or covered with small nubs, which helps them deal with all your “slippery colon mucosa.” Designing the robot was particularly tricky because of the severe constraints on the overall size of the device, which is just 3 centimeters wide and 2.3 cm high. In order to cram the two motors required for full control, they had to be arranged parallel to the treads, resulting in a fairly complex system of 3D-printed worm gears. And to make the robot actually useful, it includes a camera, LED lights, tubes for injecting air and water, and a tool port that can accommodate endoscopy instruments like forceps and snares to retrieve tissue samples.

So far, Endoculus has spent some time inside of a live pig, although it wasn’t able to get that far since pig intestines are smaller than human intestines, and because apparently the pig intestine is spiraled somehow. The pig (and the robot) both came out fine. A (presumably different) pig then provided some intestine that was expanded to human-intestine size, inside of which Endoculus did much better, and was able to zip along at up to 40 millimeters per second without causing any damage. Personally, I’m not sure I’d want a robot to explore my intestine at a speed much higher than that.

The next step with Endoculus is to add some autonomy, which means figuring out how to do localization and mapping using the robot’s onboard camera and IMU. And then of course someone has to be the first human to experience Endoculus directly, which I’d totally volunteer for except the research team is in Colorado and I’m not. Sorry!

Novel Optimization-Based Design and Surgical Evaluation of a Treaded Robotic Capsule Colonoscope,” by Gregory A. Formosa, J. Micah Prendergast, Steven A. Edmundowicz, and Mark E. Rentschler, from the University of Colorado, was presented at ICRA 2020.

Back to IEEE Journal Watch

Let’s talk about bowels! Most of us have them, most of us use them a lot, and like anything that gets used a lot, they eventually need to get checked out to help make sure that everything will keep working the way it should for as long as you need it to. Generally, this means a colonoscopy, and while there are other ways of investigating what’s going on in your gut, a camera on a flexible tube is still “the gold-standard method of diagnosis and intervention,” according to some robotics researchers who want to change that up a bit.

The University of Colorado’s Advanced Medical Technologies Lab has been working on a tank robot called Endoculus that’s able to actively drive itself through your intestines, rather than being shoved. The good news is that it’s very small, and the bad news is that it’s probably not as small as you’d like it to be.

The reason why a robot like Endoculus is necessary (or at least a good idea) is that trying to stuff a semi-rigid endoscopy tube into the semi-floppy tube that is your intestine doesn’t always go smoothly. Sometimes, the tip of the endoscopy tube can get stuck, and as more tube is fed in, it causes the intestine to distend, which best case is painful and worst case can cause serious internal injuries. One way of solving this is with swallowable camera pills, but those don’t help you with tasks like taking tissue samples. A self-propelled system like Endoculus could reduce risk while also making the procedure faster and cheaper.

Image: Advanced Medical Technologies Lab/University of Colorado The researchers say that while the width of Endoculus is larger than a traditional endoscope, the device would require “minimal distention during use” and would “not cause pain or harm to the patient.” Future versions of the robot, they add, will “yield a smaller footprint.”

Endoculus gets around with four sets of treads, angled to provide better traction against the curved walls of your gut. The treads are micropillared, or covered with small nubs, which helps them deal with all your “slippery colon mucosa.” Designing the robot was particularly tricky because of the severe constraints on the overall size of the device, which is just 3 centimeters wide and 2.3 cm high. In order to cram the two motors required for full control, they had to be arranged parallel to the treads, resulting in a fairly complex system of 3D-printed worm gears. And to make the robot actually useful, it includes a camera, LED lights, tubes for injecting air and water, and a tool port that can accommodate endoscopy instruments like forceps and snares to retrieve tissue samples.

So far, Endoculus has spent some time inside of a live pig, although it wasn’t able to get that far since pig intestines are smaller than human intestines, and because apparently the pig intestine is spiraled somehow. The pig (and the robot) both came out fine. A (presumably different) pig then provided some intestine that was expanded to human-intestine size, inside of which Endoculus did much better, and was able to zip along at up to 40 millimeters per second without causing any damage. Personally, I’m not sure I’d want a robot to explore my intestine at a speed much higher than that.

The next step with Endoculus is to add some autonomy, which means figuring out how to do localization and mapping using the robot’s onboard camera and IMU. And then of course someone has to be the first human to experience Endoculus directly, which I’d totally volunteer for except the research team is in Colorado and I’m not. Sorry!

Novel Optimization-Based Design and Surgical Evaluation of a Treaded Robotic Capsule Colonoscope,” by Gregory A. Formosa, J. Micah Prendergast, Steven A. Edmundowicz, and Mark E. Rentschler, from the University of Colorado, was presented at ICRA 2020.

Back to IEEE Journal Watch

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

CLAWAR 2020 – August 24-26, 2020 – [Online Conference] ICUAS 2020 – September 1-4, 2020 – Athens, Greece ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Online Conference] IROS 2020 – October 25-29, 2020 – Las Vegas, Nev., USA CYBATHLON 2020 – November 13-14, 2020 – [Online Event] ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA

Let us know if you have suggestions for next week, and enjoy today’s videos.

We first met Ibuki, Hiroshi Ishiguro’s latest humanoid robot, a couple of years ago. A recent video shows how Ishiguro and his team are teaching the robot to express its emotional state through gait and body posture while moving.

This paper presents a subjective evaluation of the emotions of a wheeled mobile humanoid robot expressing emotions during movement by replicating human gait-induced upper body motion. For this purpose, we proposed the robot equipped with a vertical oscillation mechanism that generates such motion by focusing on human center-of-mass trajectory. In the experiment, participants watched videos of the robot’s different emotional gait-induced upper body motions, and assess the type of emotion shown, and their confidence level in their answer.

Hiroshi Ishiguro Lab ] via [ RobotStart ]

ICYMI: This is a zinc-air battery made partly of Kevlar that can be used to support weight, not just add to it.

Like biological fat reserves store energy in animals, a new rechargeable zinc battery integrates into the structure of a robot to provide much more energy, a team led by the University of Michigan has shown.

The new battery works by passing hydroxide ions between a zinc electrode and the air side through an electrolyte membrane. That membrane is partly a network of aramid nanofibers—the carbon-based fibers found in Kevlar vests—and a new water-based polymer gel. The gel helps shuttle the hydroxide ions between the electrodes. Made with cheap, abundant and largely nontoxic materials, the battery is more environmentally friendly than those currently in use. The gel and aramid nanofibers will not catch fire if the battery is damaged, unlike the flammable electrolyte in lithium ion batteries. The aramid nanofibers could be upcycled from retired body armor.

[ University of Michigan ]

In what they say is the first large-scale study of the interactions between sound and robotic action, researchers at CMU’s Robotics Institute found that sounds could help a robot differentiate between objects, such as a metal screwdriver and a metal wrench. Hearing also could help robots determine what type of action caused a sound and help them use sounds to predict the physical properties of new objects.

[ CMU ]

Captured on Aug. 11 during the second rehearsal of the OSIRIS-REx mission’s sample collection event, this series of images shows the SamCam imager’s field of view as the NASA spacecraft approaches asteroid Bennu’s surface. The rehearsal brought the spacecraft through the first three maneuvers of the sampling sequence to a point approximately 131 feet (40 meters) above the surface, after which the spacecraft performed a back-away burn.

These images were captured over a 13.5-minute period. The imaging sequence begins at approximately 420 feet (128 meters) above the surface – before the spacecraft executes the “Checkpoint” maneuver – and runs through to the “Matchpoint” maneuver, with the last image taken approximately 144 feet (44 meters) above the surface of Bennu.

[ NASA ]

The DARPA AlphaDogfight Trials Final Event took place yesterday; the livestream is like 5 hours long, but you can skip ahead to 4:39 ish to see the AI winner take on a human F-16 pilot in simulation.

Some things to keep in mind about the result: The AI had perfect situational knowledge while the human pilot had to use eyeballs, and in particular, the AI did very well at lining up its (virtual) gun with the human during fast passing maneuvers, which is the sort of thing that autonomous systems excel at but is not necessarily reflective of better strategy.

[ DARPA ]

Coming soon from Clearpath Robotics!

[ Clearpath ]

This video introduces Preferred Networks’ Hand type A, a tendon-driven robot gripper with passively switchable underactuated surface.

[ Preferred Networks ]

CYBATHLON 2020 will take place on 13 – 14 November 2020 – at the teams’ home bases. They will set up their infrastructure for the competition and film their races. Instead of starting directly next to each other, the pilots will start individually and under the supervision of CYBATHLON officials. From Zurich, the competitions will be broadcast through a new platform in a unique live programme.

[ Cybathlon ]

In this project, we consider the task of autonomous car racing in the top-selling car racing game Gran Turismo Sport. Gran Turismo Sport is known for its detailed physics simulation of various cars and tracks. Our approach makes use of maximum-entropy deep reinforcement learning and a new reward design to train a sensorimotor policy to complete a given race track as fast as possible. We evaluate our approach in three different time trial settings with different cars and tracks. Our results show that the obtained controllers not only beat the built-in non-player character of Gran Turismo Sport, but also outperform the fastest known times in a dataset of personal best lap times of over 50,000 human drivers.

[ UZH ]

With the help of the software pitasc from Fraunhofer IPA, an assembly task is no longer programmed point by point, but workpiece-related. Thus, pitasc adapts the assembly process itself for new product variants with the help of updated parameters.

[ Fraunhofer ]

In this video, a multi-material robot simulator is used to design a shape-changing robot, which is then transferred to physical hardware. The simulated and real robots can use shape change to switch between rolling gaits and inchworm gaits, to locomote in multiple environments.

[ Yale ]

This work presents a novel loco-manipulation control framework for the execution of complex tasks with kinodynamic constraints using mobile manipulators. As a representative example, we consider the handling and re-positioning of pallet jacks in unstructured environments. While these results reveal with a proof-of- concept the effectiveness of the proposed framework, they also demonstrate the high potential of mobile manipulators for relieving human workers from such repetitive and labor intensive tasks. We believe that this extended functionality can contribute to increasing the usability of mobile manipulators in different application scenarios.

[ Paper ] via [ IIT ]

I don’t know why this dinosaur ice cream serving robot needs to blow smoke out of its nose, but I like it.

[ Connected Robotics ] via [ RobotStart ]

Guardian S remote visual inspection and surveillance robots make laying cable runs in confined or hard to reach spaces easy. With advanced maneuverability and the ability to climb vertical, ferrous surfaces, the robot reaches areas that are not always easily accessible.

[ Sarcos ]

Looks like the company that bought Anki is working on an add-on to let cars charge while they drive.

[ Digital Dream Labs ]

Chris Atkeson gives a brief talk for the CMU Robotics Institute orientation.

[ CMU RI ]

A UofT Robotics Seminar, featuring Russ Tedrake from MIT and TRI on “Feedback Control for Manipulation.”

Control theory has an answer for just about everything, but seems to fall short when it comes to closing a feedback loop using a camera, dealing with the dynamics of contact, and reasoning about robustness over the distribution of tasks one might find in the kitchen. Recent examples from RL and imitation learning demonstrate great promise, but don’t leverage the rigorous tools from systems theory. I’d like to discuss why, and describe some recent results of closing feedback loops from pixels for “category-level” robot manipulation.

[ UofT ]

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

CLAWAR 2020 – August 24-26, 2020 – [Online Conference] ICUAS 2020 – September 1-4, 2020 – Athens, Greece ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Online Conference] IROS 2020 – October 25-29, 2020 – Las Vegas, Nev., USA CYBATHLON 2020 – November 13-14, 2020 – [Online Event] ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA

Let us know if you have suggestions for next week, and enjoy today’s videos.

We first met Ibuki, Hiroshi Ishiguro’s latest humanoid robot, a couple of years ago. A recent video shows how Ishiguro and his team are teaching the robot to express its emotional state through gait and body posture while moving.

This paper presents a subjective evaluation of the emotions of a wheeled mobile humanoid robot expressing emotions during movement by replicating human gait-induced upper body motion. For this purpose, we proposed the robot equipped with a vertical oscillation mechanism that generates such motion by focusing on human center-of-mass trajectory. In the experiment, participants watched videos of the robot’s different emotional gait-induced upper body motions, and assess the type of emotion shown, and their confidence level in their answer.

Hiroshi Ishiguro Lab ] via [ RobotStart ]

ICYMI: This is a zinc-air battery made partly of Kevlar that can be used to support weight, not just add to it.

Like biological fat reserves store energy in animals, a new rechargeable zinc battery integrates into the structure of a robot to provide much more energy, a team led by the University of Michigan has shown.

The new battery works by passing hydroxide ions between a zinc electrode and the air side through an electrolyte membrane. That membrane is partly a network of aramid nanofibers—the carbon-based fibers found in Kevlar vests—and a new water-based polymer gel. The gel helps shuttle the hydroxide ions between the electrodes. Made with cheap, abundant and largely nontoxic materials, the battery is more environmentally friendly than those currently in use. The gel and aramid nanofibers will not catch fire if the battery is damaged, unlike the flammable electrolyte in lithium ion batteries. The aramid nanofibers could be upcycled from retired body armor.

[ University of Michigan ]

In what they say is the first large-scale study of the interactions between sound and robotic action, researchers at CMU’s Robotics Institute found that sounds could help a robot differentiate between objects, such as a metal screwdriver and a metal wrench. Hearing also could help robots determine what type of action caused a sound and help them use sounds to predict the physical properties of new objects.

[ CMU ]

Captured on Aug. 11 during the second rehearsal of the OSIRIS-REx mission’s sample collection event, this series of images shows the SamCam imager’s field of view as the NASA spacecraft approaches asteroid Bennu’s surface. The rehearsal brought the spacecraft through the first three maneuvers of the sampling sequence to a point approximately 131 feet (40 meters) above the surface, after which the spacecraft performed a back-away burn.

These images were captured over a 13.5-minute period. The imaging sequence begins at approximately 420 feet (128 meters) above the surface – before the spacecraft executes the “Checkpoint” maneuver – and runs through to the “Matchpoint” maneuver, with the last image taken approximately 144 feet (44 meters) above the surface of Bennu.

[ NASA ]

The DARPA AlphaDogfight Trials Final Event took place yesterday; the livestream is like 5 hours long, but you can skip ahead to 4:39 ish to see the AI winner take on a human F-16 pilot in simulation.

Some things to keep in mind about the result: The AI had perfect situational knowledge while the human pilot had to use eyeballs, and in particular, the AI did very well at lining up its (virtual) gun with the human during fast passing maneuvers, which is the sort of thing that autonomous systems excel at but is not necessarily reflective of better strategy.

[ DARPA ]

Coming soon from Clearpath Robotics!

[ Clearpath ]

This video introduces Preferred Networks’ Hand type A, a tendon-driven robot gripper with passively switchable underactuated surface.

[ Preferred Networks ]

CYBATHLON 2020 will take place on 13 – 14 November 2020 – at the teams’ home bases. They will set up their infrastructure for the competition and film their races. Instead of starting directly next to each other, the pilots will start individually and under the supervision of CYBATHLON officials. From Zurich, the competitions will be broadcast through a new platform in a unique live programme.

[ Cybathlon ]

In this project, we consider the task of autonomous car racing in the top-selling car racing game Gran Turismo Sport. Gran Turismo Sport is known for its detailed physics simulation of various cars and tracks. Our approach makes use of maximum-entropy deep reinforcement learning and a new reward design to train a sensorimotor policy to complete a given race track as fast as possible. We evaluate our approach in three different time trial settings with different cars and tracks. Our results show that the obtained controllers not only beat the built-in non-player character of Gran Turismo Sport, but also outperform the fastest known times in a dataset of personal best lap times of over 50,000 human drivers.

[ UZH ]

With the help of the software pitasc from Fraunhofer IPA, an assembly task is no longer programmed point by point, but workpiece-related. Thus, pitasc adapts the assembly process itself for new product variants with the help of updated parameters.

[ Fraunhofer ]

In this video, a multi-material robot simulator is used to design a shape-changing robot, which is then transferred to physical hardware. The simulated and real robots can use shape change to switch between rolling gaits and inchworm gaits, to locomote in multiple environments.

[ Yale ]

This work presents a novel loco-manipulation control framework for the execution of complex tasks with kinodynamic constraints using mobile manipulators. As a representative example, we consider the handling and re-positioning of pallet jacks in unstructured environments. While these results reveal with a proof-of- concept the effectiveness of the proposed framework, they also demonstrate the high potential of mobile manipulators for relieving human workers from such repetitive and labor intensive tasks. We believe that this extended functionality can contribute to increasing the usability of mobile manipulators in different application scenarios.

[ Paper ] via [ IIT ]

I don’t know why this dinosaur ice cream serving robot needs to blow smoke out of its nose, but I like it.

[ Connected Robotics ] via [ RobotStart ]

Guardian S remote visual inspection and surveillance robots make laying cable runs in confined or hard to reach spaces easy. With advanced maneuverability and the ability to climb vertical, ferrous surfaces, the robot reaches areas that are not always easily accessible.

[ Sarcos ]

Looks like the company that bought Anki is working on an add-on to let cars charge while they drive.

[ Digital Dream Labs ]

Chris Atkeson gives a brief talk for the CMU Robotics Institute orientation.

[ CMU RI ]

A UofT Robotics Seminar, featuring Russ Tedrake from MIT and TRI on “Feedback Control for Manipulation.”

Control theory has an answer for just about everything, but seems to fall short when it comes to closing a feedback loop using a camera, dealing with the dynamics of contact, and reasoning about robustness over the distribution of tasks one might find in the kitchen. Recent examples from RL and imitation learning demonstrate great promise, but don’t leverage the rigorous tools from systems theory. I’d like to discuss why, and describe some recent results of closing feedback loops from pixels for “category-level” robot manipulation.

[ UofT ]

Visual reasoning is a critical stage in visual question answering (Antol et al., 2015), but most of the state-of-the-art methods categorized the VQA tasks as a classification problem without taking the reasoning process into account. Various approaches are proposed to solve this multi-modal task that requires both abilities of comprehension and reasoning. The recently proposed neural module network (Andreas et al., 2016b), which assembles the model with a few primitive modules, is capable of performing a spatial or arithmetical reasoning over the input image to answer the questions. Nevertheless, its performance is not satisfying especially in the real-world datasets (e.g., VQA 1.0& 2.0) due to its limited primitive modules and suboptimal layout. To address these issues, we propose a novel method of Dual-Path Neural Module Network which can implement complex visual reasoning by forming a more flexible layout regularized by the pairwise loss. Specifically, we first use the region proposal network to generate both visual and spatial information, which helps it perform spatial reasoning. Then, we advocate to process a pair of different images along with the same question simultaneously, named as a “complementary pair,” which encourages the model to learn a more reasonable layout by suppressing the overfitting to the language priors. The model can jointly learn the parameters in the primitive module and the layout generation policy, which is further boosted by introducing a novel pairwise reward. Extensive experiments show that our approach significantly improves the performance of neural module networks especially on the real-world datasets.

It’s no secret that one of the most significant constraints on robots is power. Most robots need lots of it, and it has to come from somewhere, with that somewhere usually being a battery because there simply aren’t many other good options. Batteries, however, are famous for having poor energy density, and the smaller your robot is, the more of a problem this becomes. And the issue with batteries goes beyond the battery itself, but also carries over into all the other components that it takes to turn the stored energy into useful work, which again is a particular problem for small-scale robots.

In a paper published this week in Science Robotics, researchers from the University of Southern California, in Los Angeles, demonstrate RoBeetle, an 88-milligram four legged robot that runs entirely on methanol, a power-dense liquid fuel. Without any electronics at all, it uses an exceptionally clever bit of mechanical autonomy to convert methanol vapor directly into forward motion, one millimeter-long step at a time.

It’s not entirely clear from the video how the robot actually works, so let’s go through how it’s put together, and then look at the actuation cycle.

Image: Science Robotics RoBeetle (A) uses a methanol-based actuation mechanism (B). The robot’s body (C) includes the fuel tank subassembly (D), a tank lid, transmission, and sliding shutter (E), bottom side of the sliding shutter (F), nickel-titanium-platinum composite wire and leaf spring (G), and front legs and hind legs with bioinspired backward-oriented claws (H).

The body of RoBeetle is a boxy fuel tank that you can fill with methanol by poking a syringe through a fuel inlet hole. It’s a quadruped, more or less, with fixed hind legs and two front legs attached to a single transmission that moves them both at once in a sort of rocking forward and up followed by backward and down motion. The transmission is hooked up to a leaf spring that’s tensioned to always pull the legs backward, such that when the robot isn’t being actuated, the spring and transmission keep its front legs more or less vertical and allow the robot to stand. Those horns are primarily there to hold the leaf spring in place, but they’ve got little hooks that can carry stuff, too.

The actuator itself is a nickel-titanium (NiTi) shape-memory alloy (SMA), which is just a wire that gets longer when it heats up and then shrinks back down when it cools. SMAs are fairly common and used for all kinds of things, but what makes this particular SMA a little different is that it’s been messily coated with platinum. The “messily” part is important for a reason that we’ll get to in just a second.

The way that the sliding vent is attached to the transmission is the really clever bit about this robot, because it means that the motion of the wire itself is used to modulate the flow of fuel through a purely mechanical system. Essentially, it’s an actuator and a sensor at the same time.

One end of the SMA wire is attached to the middle of the leaf spring, while the other end runs above the back of the robot where it’s stapled to an anchor block on the robot’s rear end. With the SMA wire hooked up but not actuated (i.e., cold rather than warm), it’s short enough that the leaf spring gets pulled back, rocking the legs forward and up. The last component is embedded in the robot’s back, right along the spine and directly underneath the SMA actuator. It’s a sliding vent attached to the transmission, so that the vent is open when the SMA wire is cold and the leaf spring is pulled back, and closed when the SMA wire is warm and the leaf spring is relaxed. The way that the sliding vent is attached to the transmission is the really clever bit about this robot, because it means that the motion of the wire itself is used to modulate the flow of fuel through a purely mechanical system. Essentially, it’s an actuator and a sensor at the same time.

The actuation cycle that causes the robot to walk begins with a full fuel tank and a cold SMA wire. There’s tension on the leaf spring, pulling the transmission back and rocking the legs forward and upward. The transmission also pulls the sliding vent into the open position, allowing methanol vapor to escape up out of the fuel tank and into the air, where it wafts past the SMA wire that runs directly above the vent. 

The platinum facilitates a reaction of the methanol (CH3OH) with oxygen in the air (combustion, although not the dramatic flaming and explosive kind) to generate a couple of water molecules and some carbon dioxide plus a bunch of heat, and this is where the messy platinum coating is important, because messy means lots of surface area for the platinum to interact with as much methanol as possible. In just a second or two the temperature of the SMA wire skyrockets from 50 to 100 ºC and it expands, allowing the leaf spring about 0.1 mm of slack. As the leaf spring relaxes, the transmission moves the legs backwards and downwards, and the robot pulls itself forward about 1.2 mm. At the same time, the transmission is closing off the sliding vent, cutting off the supply of methanol vapor. Without the vapor reacting with the platinum and generating heat, in about a second and a half, the SMA wire cools down. As it does, it shrinks, pulling on the leaf spring and starting the cycle over again. Top speed is 0.76 mm/s (0.05 body-lengths per second).

An interesting environmental effect is that the speed of the robot can be enhanced by a gentle breeze. This is because air moving over the SMA wire cools it down a bit faster while also blowing away any residual methanol from around the vents, shutting down the reaction more completely. RoBeetle can carry more than its own body weight in fuel, and it takes approximately 155 minutes for a full tank of methanol to completely evaporate. It’s worth noting that despite the very high energy density of methanol, this is actually a stupendously inefficient way of powering a robot, with an estimated end-to-end efficiency of just 0.48 percent. Not 48 percent, mind you, but 0.48 percent, while in general, powering SMAs with electricity is much more efficient.

However, you have to look at the entire system that would be necessary to deliver that electricity, and for a robot as small as RoBeetle, the researchers say that it’s basically impossible. The lightest commercially available battery and power supply that would deliver enough juice to heat up an SMA actuator weighs about 800 mg, nearly 10 times the total weight of RoBeetle itself. From that perspective, RoBeetle’s efficiency is actually pretty good. 

Image: A. Kitterman/Science Robotics; adapted from R.L.T./MIT Comparison of various untethered microrobots and bioinspired soft robots that use different power and actuation strategies.

There are some other downsides to RoBeetle we should mention—it can only move forwards, not backwards, and it can’t steer. Its speed isn’t adjustable, and once it starts walking, it’ll walk until it either breaks or runs out of fuel. The researchers have some ideas about the speed, at least, pointing out that increasing the speed of fuel delivery by using pressurized liquid fuels like butane or propane would increase the actuator output frequency. And the frequency, amplitude, and efficiency of the SMAs themselves can be massively increased “by arranging multiple fiber-like thin artificial muscles in hierarchical configurations similar to those observed in sarcomere-based animal muscle,” making RoBeetle even more beetle-like.

As for sensing, RoBeetle’s 230-mg payload is enough to carry passive sensors, but getting those sensors to usefully interact with the robot itself to enable any kind of autonomy remains a challenge. Mechanically intelligence is certainly possible, though, and we can imagine RoBeetle adopting some of the same sorts of systems that have been proposed for the clockwork rover that JPL wants to use for Venus exploration. The researchers also mention how RoBeetle could potentially serve as a model for microbots capable of aerial locomotion, which is something we’d very much like to see.

An 88-milligram insect-scale autonomous crawling robot driven by a catalytic artificial muscle,” by Xiufeng Yang, Longlong Chang, and Néstor O. Pérez-Arancibia from University of Southern California, in Los Angeles, was published in Science Robotics.

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