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Research in creative robotics continues to expand across all creative domains, including art, music and language. Creative robots are primarily designed to be task specific, with limited research into the implications of their design outside their core task. In the case of a musical robot, this includes when a human sees and interacts with the robot before and after the performance, as well as in between pieces. These non-musical interaction tasks such as the presence of a robot during musical equipment set up, play a key role in the human perception of the robot however have received only limited attention. In this paper, we describe a new audio system using emotional musical prosody, designed to match the creative process of a musical robot for use before, between and after musical performances. Our generation system relies on the creation of a custom dataset for musical prosody. This system is designed foremost to operate in real time and allow rapid generation and dialogue exchange between human and robot. For this reason, the system combines symbolic deep learning through a Conditional Convolution Variational Auto-encoder, with an emotion-tagged audio sampler. We then compare this to a SOTA text-to-speech system in our robotic platform, Shimon the marimba player.We conducted a between-groups study with 100 participants watching a musician interact for 30 s with Shimon. We were able to increase user ratings for the key creativity metrics; novelty and coherence, while maintaining ratings for expressivity across each implementation. Our results also indicated that by communicating in a form that relates to the robot’s core functionality, we can raise likeability and perceived intelligence, while not altering animacy or anthropomorphism. These findings indicate the variation that can occur in the perception of a robot based on interactions surrounding a performance, such as initial meetings and spaces between pieces, in addition to the core creative algorithms.

Stimuli-responsive hydrogels are candidate building blocks for soft robotic applications due to many of their unique properties, including tunable mechanical properties and biocompatibility. Over the past decade, there has been significant progress in developing soft and biohybrid actuators using naturally occurring and synthetic hydrogels to address the increasing demands for machines capable of interacting with fragile biological systems. Recent advancements in three-dimensional (3D) printing technology, either as a standalone manufacturing process or integrated with traditional fabrication techniques, have enabled the development of hydrogel-based actuators with on-demand geometry and actuation modalities. This mini-review surveys existing research efforts to inspire the development of novel fabrication techniques using hydrogel building blocks and identify potential future directions. In this article, existing 3D fabrication techniques for hydrogel actuators are first examined. Next, existing actuation mechanisms, including pneumatic, hydraulic, ionic, dehydration-rehydration, and cell-powered actuation, are reviewed with their benefits and limitations discussed. Subsequently, the applications of hydrogel-based actuators, including compliant handling of fragile items, micro-swimmers, wearable devices, and origami structures, are described. Finally, challenges in fabricating functional actuators using existing techniques are discussed.

In modular robotics modules can be reconfigured to change the morphology of the robot, making it able to adapt to specific tasks. However, optimizing both the body and control of such robots is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. These challenges can trap many optimization algorithms in local optima, halting progress towards better solutions. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize high performing and diverse morphologies and controllers in modular robotics. We compare two objective-based search algorithms, with and without a diversity promoting objective, with a Quality Diversity algorithm—MAP-Elites. The results show that MAP-Elites is capable of evolving the highest performing solutions in addition to generating the largest morphological diversity. Further, MAP-Elites is superior at regaining performance when transferring the population to new and more difficult environments. By analyzing genealogical ancestry we show that MAP-Elites produces more diverse and higher performing stepping stones than the two other objective-based search algorithms. The experiments transitioning the populations to new environments show the utility of morphological diversity, while the analysis of stepping stones show a strong correlation between diversity of ancestry and maximum performance on the locomotion task. Together, these results demonstrate the suitability of MAP-elites for the challenging task of morphology-control search for modular robots, and shed light on the algorithm’s capability of generating stepping stones for reaching high-performing solutions.

Drone autonomy is getting more and more impressive, but we’re starting to get to the point where it’s getting significantly more difficult to improve on existing capabilities. Companies like Skydio are selling (for cheap!) commercial drones that have no problem dynamically path planning around obstacles at high speeds while tracking you, which is pretty amazing, and it can also autonomously create 3D maps of structures. In both of these cases, there’s a human indirectly in the loop, either saying “follow me” or “map this specific thing.” In other words, the level of autonomous flight is very high, but there’s still some reliance on a human for high-level planning. Which, for what Skydio is doing, is totally fine and the right way to do it.

Exyn, a drone company with roots in the GRASP Lab at the University of Pennsylvania, has been developing drones for inspections of large unstructured spaces like mines. This is an incredibly challenging environment, being GPS-denied, dark, dusty, and dangerous, to name just a few of the challenges. While Exyn’s lidar-equipped drones have been autonomous for a while now, they’re now able to operate without any high-level planning from a human at all. At this level of autonomy, which Exyn calls Level 4A, the operator simply defines a volume for the drone to map, and then from takeoff to landing, the drone will methodically explore the entire space and generate a high resolution map all by itself, even if it goes far beyond communications range to do so.

Let’s be specific about what “Level 4A” autonomy means, because until now, there haven’t really been established autonomy levels for drones. And the reason that there are autonomy levels for drones all of a sudden is because Exyn just went ahead and invented some. To be fair, Exyn took inspiration from the SAE autonomy levels, so there is certainly some precedent here, but it’s still worth keeping in mind that this whole system is for the moment just something that Exyn came up with by themselves and applied to their own system. They did put a bunch of thought into it, at least, and you can read a whitepaper on the whole thing here.

Graphic: Exyn Larger version here.

A couple things about exactly what Exyn is doing: Their drone, which carries lights, a GoPro, some huge computing power, an even huger battery, and a rotating Velodyne lidar, is able to operate completely independently of a human operator or really any kind of external inputs at all. No GPS, no base station, no communications, no prior understanding of the space, nothing. You tell the drone where you want it to map, and it’ll take off and then decide on its own where and how to explore the space that it’s in, building up an obscenely high resolution lidar map as it goes and continuously expanding that map until it runs out of unexplored areas, at which point it’ll follow the map back home and land itself. “When we’re executing the exploration,” Exyn CTO Jason Derenick tells us, “what we’re doing is finding the boundary between the visible and explored space, and the unknown space. We then compute viewpoint candidates, which are locations along that boundary where we can infer how much potential information our sensors can gain, and then the system selects the one with the most opportunity for seeing as much of the environment as possible.”

Flying at up to 2 m/s, Exyn’s drone can explore 16 million cubic meters in a single flight (about nine football stadiums worth of volume), and if the area you want it to explore is larger than that, it can go back out for more rounds after a battery swap.

It’s important to understand, though, what the limitations of this drone’s autonomy are. We’re told that it can sense things like power lines, although probably not something narrow like fishing wire. Which so far hasn’t been a problem, because it’s an example of a “pathological” obstacle—something that is not normal, and would typically only be encountered if it was placed there specifically to screw you up. Dynamic obstacles (like humans or vehicles) moving at walking speed are also fine. Dust can be tricky at times, although the drone can identify excessive amounts of dust in the air, and it’ll wait a bit for the dust to settle before updating its map.

Photo; Exyn

The commercial applications of a totally hands-off system that’s able to autonomously generate detailed lidar maps of unconstrained spaces in near real-time are pretty clear. But what we’re most excited about are the potential search and rescue use cases, especially when Exyn starts to get multiple drones working together collaboratively. You can imagine a situation in which you need to find a lost person in a cave or a mine, and you unload a handful of drones at the entrance, tell them “go explore until you find a human,” and then just let them do their thing.

To make this happen, though, Exyn will need to add an additional level of understanding to their system, which is something they’re working on now, says Derenick. This means both understanding what objects are, as well as reasoning about them, which could mean what the object represents in a more abstract sense as well as how things like dynamic obstacles may move. Autonomous cars have to do this routinely, but for a drone with severe size and power constraints, it’s a much bigger challenge, but one that I’m pretty sure Exyn will figure out.

Drone autonomy is getting more and more impressive, but we’re starting to get to the point where it’s getting significantly more difficult to improve on existing capabilities. Companies like Skydio are selling (for cheap!) commercial drones that have no problem dynamically path planning around obstacles at high speeds while tracking you, which is pretty amazing, and it can also autonomously create 3D maps of structures. In both of these cases, there’s a human indirectly in the loop, either saying “follow me” or “map this specific thing.” In other words, the level of autonomous flight is very high, but there’s still some reliance on a human for high-level planning. Which, for what Skydio is doing, is totally fine and the right way to do it.

Exyn, a drone company with roots in the GRASP Lab at the University of Pennsylvania, has been developing drones for inspections of large unstructured spaces like mines. This is an incredibly challenging environment, being GPS-denied, dark, dusty, and dangerous, to name just a few of the challenges. While Exyn’s lidar-equipped drones have been autonomous for a while now, they’re now able to operate without any high-level planning from a human at all. At this level of autonomy, which Exyn calls Level 4A, the operator simply defines a volume for the drone to map, and then from takeoff to landing, the drone will methodically explore the entire space and generate a high resolution map all by itself, even if it goes far beyond communications range to do so.

Let’s be specific about what “Level 4A” autonomy means, because until now, there haven’t really been established autonomy levels for drones. And the reason that there are autonomy levels for drones all of a sudden is because Exyn just went ahead and invented some. To be fair, Exyn took inspiration from the SAE autonomy levels, so there is certainly some precedent here, but it’s still worth keeping in mind that this whole system is for the moment just something that Exyn came up with by themselves and applied to their own system. They did put a bunch of thought into it, at least, and you can read a whitepaper on the whole thing here.

Graphic: Exyn Larger version here.

A couple things about exactly what Exyn is doing: Their drone, which carries lights, a GoPro, some huge computing power, an even huger battery, and a rotating Velodyne lidar, is able to operate completely independently of a human operator or really any kind of external inputs at all. No GPS, no base station, no communications, no prior understanding of the space, nothing. You tell the drone where you want it to map, and it’ll take off and then decide on its own where and how to explore the space that it’s in, building up an obscenely high resolution lidar map as it goes and continuously expanding that map until it runs out of unexplored areas, at which point it’ll follow the map back home and land itself. “When we’re executing the exploration,” Exyn CTO Jason Derenick tells us, “what we’re doing is finding the boundary between the visible and explored space, and the unknown space. We then compute viewpoint candidates, which are locations along that boundary where we can infer how much potential information our sensors can gain, and then the system selects the one with the most opportunity for seeing as much of the environment as possible.”

Flying at up to 2 m/s, Exyn’s drone can explore 16 million cubic meters in a single flight (about nine football stadiums worth of volume), and if the area you want it to explore is larger than that, it can go back out for more rounds after a battery swap.

It’s important to understand, though, what the limitations of this drone’s autonomy are. We’re told that it can sense things like power lines, although probably not something narrow like fishing wire. Which so far hasn’t been a problem, because it’s an example of a “pathological” obstacle—something that is not normal, and would typically only be encountered if it was placed there specifically to screw you up. Dynamic obstacles (like humans or vehicles) moving at walking speed are also fine. Dust can be tricky at times, although the drone can identify excessive amounts of dust in the air, and it’ll wait a bit for the dust to settle before updating its map.

Photo; Exyn

The commercial applications of a totally hands-off system that’s able to autonomously generate detailed lidar maps of unconstrained spaces in near real-time are pretty clear. But what we’re most excited about are the potential search and rescue use cases, especially when Exyn starts to get multiple drones working together collaboratively. You can imagine a situation in which you need to find a lost person in a cave or a mine, and you unload a handful of drones at the entrance, tell them “go explore until you find a human,” and then just let them do their thing.

To make this happen, though, Exyn will need to add an additional level of understanding to their system, which is something they’re working on now, says Derenick. This means both understanding what objects are, as well as reasoning about them, which could mean what the object represents in a more abstract sense as well as how things like dynamic obstacles may move. Autonomous cars have to do this routinely, but for a drone with severe size and power constraints, it’s a much bigger challenge, but one that I’m pretty sure Exyn will figure out.

The development of a hybrid system for people with spinal cord injuries is described. The system includes implanted neural stimulation to activate the user's otherwise paralyzed muscles, an exoskeleton with electromechanical actuators at the hips and knees, and a sensory and control system that integrates both components. We are using a muscle-first approach: The person's muscles are the primary motivator for his/her joints and the motors provide power assistance. This design philosophy led to the development of high efficiency, low friction joint actuators, and feed-forward, burst-torque control. The system was tested with two participants with spinal cord injury (SCI) and unique implanted stimulation systems. Torque burst addition was found to increase gait speed. The system was found to satisfy the main design requirements as laid out at the outset.

While the privacy implications of social robots have been increasingly discussed and privacy-sensitive robotics is becoming a research field within human–robot interaction, little empirical research has investigated privacy concerns about robots and the effect they have on behavioral intentions. To address this gap, we present the results of an experimental vignette study that includes antecedents from the privacy, robotics, technology adoption, and trust literature. Using linear regression analysis, with the privacy-invasiveness of a fictional but realistic robot as the key manipulation, we show that privacy concerns affect use intention significantly and negatively. Compared with earlier work done through a survey, where we found a robot privacy paradox, the experimental vignette approach allows for a more realistic and tangible assessment of respondents' concerns and behavioral intentions, showing how potential robot users take into account privacy as consideration for future behavior. We contextualize our findings within broader debates on privacy and data protection with smart technologies.

The continuous increase in population and human migration to urban and coastal areas leads to the expansion of built environments over natural habitats. Current infrastructure suffers from environmental changes and their impact on ecosystem services. Foundations are static anchoring structures dependent on soil compaction, which reduces water infiltration and increases flooding. Coastal infrastructure reduces wave action and landward erosion but alters natural habitat and sediment transport. On the other hand, root systems are multifunctional, resilient, biological structures that offer promising strategies for the design of civil and coastal infrastructure, such as adaptivity, multifunctionality, self-healing, mechanical and chemical soil attachment. Therefore, the biomimetic methodology is employed to abstract root strategies of interest for the design of building foundations and coastal infrastructures that prevent soil erosion, anchor structures, penetrate soils, and provide natural habitat. The strategies are described in a literature review on root biology, then these principles are abstracted from their biological context to show their potential for engineering transfer. After a review of current and developing technologies in both application fields, the abstracted strategies are translated into conceptual designs for foundation and coastal engineering. In addition to presenting the potential of root-inspired designs for both fields, this paper also showcases the main steps of the biomimetic methodology from the study of a biological system to the development of conceptual technical designs. In this way the paper also contributes to the development of a more strategic intersection between biology and engineering and provides a framework for further research and development projects.

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!):

ICRA 2021 – May 30-5, 2021 – [Online Event] RoboCup 2021 – June 22-28, 2021 – [Online Event] DARPA SubT Finals – September 21-23, 2021 – Louisville, KY, USA WeRobot 2021 – September 23-25, 2021 – Coral Gables, FL, USA ROSCon 20201 – October 21-23, 2021 – New Orleans, LA, USA

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

Within the last four days, the Ingenuity has flown twice (!) on Mars.

This is an enhanced video showing some of the dust that the helicopter kicked up as it took off:

Data is still incoming for the second flight, but we know that it went well, at least:

[ NASA ]

Can someone who knows a lot about HRI please explain to me why I'm absolutely fascinated by Flatcat?

You can now back Flatcat on Kickstarter for a vaguely distressing $1,200.

[ Flatcat ]

Digit navigates a novel indoor environment without pre-mapping or markers, with dynamic obstacle avoidance. Waypoints are defined relative to the global reference frame determined at power-on. No bins were harmed in filming.

[ Agility Robotics ]

The Yellow Drum Machine, popped up on YouTube again this week for some reason. And it's still one of my favorite robots of all time.

[ Robotshop ]

This video shows results of high-speed autonomous flight in a forest through trees. Path planning uses a trajectory library with pre-established correspondences for collision checking. Decisions are made in 0.2-0.3ms enabling the flight at the speed of 10m/s. No prior map is used.

[ Near Earth ]

We present ManipulaTHOR, a framework that facilitates visual manipulation of objects using a robotic arm. Our framework is built upon a physics engine and enables realistic interactions with objects while navigating through scenes and performing tasks.

[ Allen Institute ]

Well this is certainly one of the more unusual multirotor configurations I've ever seen.

[ KAIST ]

Thailand’s Mahidol University and the Institute of Molecular Biosciences chose ABB's YuMi cobot & IRB 1100 robot to work together to fast-track Covid-19 vaccine development. The robots quickly perform repetitive tasks such as unscrewing vials and transporting them to test stations, protecting human workers from injury or harm.

[ ABB ]

Skydio's 3D scan functionality is getting more and more impressive.

[ Skydio ]

With more than 50 service locations across Europe, Stadler Service is focused on increasing train availability, reliability, and safety. ANYbotics is partnering with Stadler Service to explore the potential of mobile robots to increase the efficiency and quality of routine inspection and maintenance of rolling stock.

[ ANYbotics ]

Inspection engineers at Kiwa Inspecta used the Elios 2 to inspect a huge decommissioned oil cavern. The inspection would have required six months and a million Euros if conducted manually but with the Elios 2 it was completed in just a few days at a significantly lower cost.

[ Flyability ]

RightHand Robotics builds a data-driven intelligent piece-picking platform, providing flexible and scalable automation for predictable order fulfillment. RightPick™ 3 is the newest generation of our award-winning autonomous, industrial robot system.

[ RightHand Robotics ]

NASA's Unmanned Aircraft Systems Traffic Management project, or UTM, is working to safely integrate drones into low-altitude airspace. In 2019, the project completed its final phase of flight tests. The research results are being transferred to the Federal Aviation Administration, who will continue development of the UTM system and implement it over time.

[ NASA ]

At the Multi-Robot Planning and Control lab, our research vision is to build multi-robot systems that are capable of acting competently in the real world. We study, develop and combine automated planning, coordination, and control methods to achieve this capability. We find that some of the most interesting basic research questions derive from the problem features and constraints imposed by real-world applications. This video illustrates some of these research questions.

[ Örebro ]

Thanks Fan!

The University of Texas at Austin’s Cockrell School of Engineering and College of Natural Sciences are partnering on life-changing research in artificial intelligence and robotics—ensuring that UT continues to lead the way in launching tomorrow’s technologies.

[ UT Robotics ]

Thanks Fan!

Over the past ten years various robotics and remote technologies have been introduced at Fukushima sites for such tasks as inspection, rubble removal, and sampling showing success and revealing challenges. Successful decommissioning will rely on the development of highly reliable robotic technologies that can be deployed rapidly and efficiently into the sites. The discussion will focus on the decommissioning challenges and robotic technologies that have been used in Fukushima. The panel will conclude with the lessons learned from Fukushima’s past 10-year experience and how robotics must prepare to be ready to respond in the event of future disasters.

[ IFRR ]

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!):

ICRA 2021 – May 30-5, 2021 – [Online Event] RoboCup 2021 – June 22-28, 2021 – [Online Event] DARPA SubT Finals – September 21-23, 2021 – Louisville, KY, USA WeRobot 2021 – September 23-25, 2021 – Coral Gables, FL, USA ROSCon 20201 – October 21-23, 2021 – New Orleans, LA, USA

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

Within the last four days, the Ingenuity has flown twice (!) on Mars.

This is an enhanced video showing some of the dust that the helicopter kicked up as it took off:

Data is still incoming for the second flight, but we know that it went well, at least:

[ NASA ]

Can someone who knows a lot about HRI please explain to me why I'm absolutely fascinated by Flatcat?

You can now back Flatcat on Kickstarter for a vaguely distressing $1,200.

[ Flatcat ]

Digit navigates a novel indoor environment without pre-mapping or markers, with dynamic obstacle avoidance. Waypoints are defined relative to the global reference frame determined at power-on. No bins were harmed in filming.

[ Agility Robotics ]

The Yellow Drum Machine, popped up on YouTube again this week for some reason. And it's still one of my favorite robots of all time.

[ Robotshop ]

This video shows results of high-speed autonomous flight in a forest through trees. Path planning uses a trajectory library with pre-established correspondences for collision checking. Decisions are made in 0.2-0.3ms enabling the flight at the speed of 10m/s. No prior map is used.

[ Near Earth ]

We present ManipulaTHOR, a framework that facilitates visual manipulation of objects using a robotic arm. Our framework is built upon a physics engine and enables realistic interactions with objects while navigating through scenes and performing tasks.

[ Allen Institute ]

Well this is certainly one of the more unusual multirotor configurations I've ever seen.

[ KAIST ]

Thailand’s Mahidol University and the Institute of Molecular Biosciences chose ABB's YuMi cobot & IRB 1100 robot to work together to fast-track Covid-19 vaccine development. The robots quickly perform repetitive tasks such as unscrewing vials and transporting them to test stations, protecting human workers from injury or harm.

[ ABB ]

Skydio's 3D scan functionality is getting more and more impressive.

[ Skydio ]

With more than 50 service locations across Europe, Stadler Service is focused on increasing train availability, reliability, and safety. ANYbotics is partnering with Stadler Service to explore the potential of mobile robots to increase the efficiency and quality of routine inspection and maintenance of rolling stock.

[ ANYbotics ]

Inspection engineers at Kiwa Inspecta used the Elios 2 to inspect a huge decommissioned oil cavern. The inspection would have required six months and a million Euros if conducted manually but with the Elios 2 it was completed in just a few days at a significantly lower cost.

[ Flyability ]

RightHand Robotics builds a data-driven intelligent piece-picking platform, providing flexible and scalable automation for predictable order fulfillment. RightPick™ 3 is the newest generation of our award-winning autonomous, industrial robot system.

[ RightHand Robotics ]

NASA's Unmanned Aircraft Systems Traffic Management project, or UTM, is working to safely integrate drones into low-altitude airspace. In 2019, the project completed its final phase of flight tests. The research results are being transferred to the Federal Aviation Administration, who will continue development of the UTM system and implement it over time.

[ NASA ]

At the Multi-Robot Planning and Control lab, our research vision is to build multi-robot systems that are capable of acting competently in the real world. We study, develop and combine automated planning, coordination, and control methods to achieve this capability. We find that some of the most interesting basic research questions derive from the problem features and constraints imposed by real-world applications. This video illustrates some of these research questions.

[ Örebro ]

Thanks Fan!

The University of Texas at Austin’s Cockrell School of Engineering and College of Natural Sciences are partnering on life-changing research in artificial intelligence and robotics—ensuring that UT continues to lead the way in launching tomorrow’s technologies.

[ UT Robotics ]

Thanks Fan!

Over the past ten years various robotics and remote technologies have been introduced at Fukushima sites for such tasks as inspection, rubble removal, and sampling showing success and revealing challenges. Successful decommissioning will rely on the development of highly reliable robotic technologies that can be deployed rapidly and efficiently into the sites. The discussion will focus on the decommissioning challenges and robotic technologies that have been used in Fukushima. The panel will conclude with the lessons learned from Fukushima’s past 10-year experience and how robotics must prepare to be ready to respond in the event of future disasters.

[ IFRR ]

Neural control might one day help patients operate robotic prosthetics by thought. Now researchers find that with the help of physical therapy, patients could accomplish more with such neural control than scientists previously knew was possible.

Around the world, research teams are developing lower-body exoskeletons to help people walk. These devices are essentially walking robots users can strap to their legs to help them move.

These exoskeletons can often automatically perform preprogrammed cyclic motions such as walking. However, when it comes to helping patients with more complex activities, patients should ideally be able to control these robotic prosthetics by thought—for example, using sensors attached to legs that can detect bioelectric signals sent from your brain to your muscles telling them to move.

“Autonomous control works really well for walking, but when it comes to more than just walking, such as playing tennis or freestyle dancing, it'd be good to have neural control,” says study senior author Helen Huang, a biomedical engineer at North Carolina State University.

One question when it comes to neural control over robotic prosthetics is how well the nervous systems of patients can still activate the muscles that amputees still have left in a limb.

"During surgery, the original structures of muscles are changed," Huang says. "We've found that people can activate these residual muscles, but the way they contract them is different from that of an able-bodied person, so they need training on how to use these muscles."

In the new study, Huang and her colleagues had an amputee with a neurally controlled powered prosthetic ankle train with a physical therapist to practice tasks that are challenging with typical prostheses. The prosthetic received bioelectric signals from two residual calf muscles responsible for controlling ankle motion.

The 57-year-old volunteer lost his left leg about halfway between the knee and the ankle. He had five training sessions with a physical therapist, each lasting about two hours, over the course of two-and-a-half weeks. The physical therapist helped provide the volunteer feedback on what the joint was doing, and trained him “first on joint-level movements, and then full-body movements and full-body coordination,” Fleming says.

After training, the volunteer could perform a variety of tasks he found difficult before. These included going from sitting to standing without any external assistance, or squatting to pick up something off the ground without compensating for the motion with other body parts.

In addition, improvements in the volunteer's stability exceeded expectations, whether he was standing or moving. Amputees wearing lower-limb robotic prostheses often experience less stability while standing, as it is difficult for the machines to predict any disturbances or the ways in which a person might anticipate and compensate for such disruptions.

“That stability and subtle control while standing was pretty surprising,” says study lead author Aaron Fleming, a biomedical engineer at North Carolina State University.

The researchers now aim to examine more patients with robotic prosthetics and test them with more tasks, such as avoiding obstacles. They also want to investigate what the nervous systems of these volunteers might be doing during such training. “Are they restoring their original neural pathways?” Huang asks.

The scientists detailed their findings in a paper published this month in the journal Wearable Technologies.

Miniature multi-rotors are promising robots for navigating subterranean networks, but maintaining a radio connection underground is challenging. In this paper, we introduce a distributed algorithm, called U-Chain (for Underground-chain), that coordinates a chain of flying robots between an exploration drone and an operator. Our algorithm only uses the measurement of the signal quality between two successive robots and an estimate of the ground speed based on an optic flow sensor. It leverages a distributed policy for each UAV and a Kalman filter to get reliable estimates of the signal quality. We evaluate our approach formally and in simulation, and we describe experimental results with a chain of 3 real miniature quadrotors (12 by 12 cm) and a base station.

This paper aims to discuss the possible role of inner speech in influencing trust in human–automation interaction. Inner speech is an everyday covert inner monolog or dialog with oneself, which is essential for human psychological life and functioning as it is linked to self-regulation and self-awareness. Recently, in the field of machine consciousness, computational models using different forms of robot speech have been developed that make it possible to implement inner speech in robots. As is discussed, robot inner speech could be a new feature affecting human trust by increasing robot transparency and anthropomorphism.

Dynamic locomotion of a quadruped robot emerges from interaction between the robot body and the terrain. When the robot has a soft body, dynamic locomotion can be realized using a simple controller. This study investigates dynamic turning of a soft quadruped robot by changing the phase difference among the legs of the robot. We develop a soft quadruped robot driven by McKibben pneumatic artificial muscles. The phase difference between the hind and fore legs is fixed whereas that between the left and right legs is changed to enable the robot to turn dynamically. Since the robot legs are soft, the contact pattern between the legs and the terrain can be varied adaptively by simply changing the phase difference. Experimental results demonstrate that changes in the phase difference lead to changes in the contact time of the hind legs, and as a result, the soft robot can turn dynamically.

Over the last few weeks, we’ve posted several articles about the next generation of warehouse manipulation robots designed to handle the non-stop stream of boxes that provide the foundation for modern ecommerce. But once these robots take boxes out of the back of a trailer or off of a pallet, there are yet more robots ready to autonomously continue the flow through a warehouse or distribution center. One of the beefiest of these autonomous mobile robots is the OTTO 1500, which is called the OTTO 1500 because (you guessed it) it can handle 1500 kg of cargo. Plus another 400kg of cargo, for a total of 1900 kg of cargo. Yeah, I don’t get it either. Anyway, it’s undergone a major update, which is a good excuse for us to ask OTTO CTO Ryan Gariepy some questions about it.

The earlier version, also named OTTO 1500, has over a million hours of real-world operation, which is impressive. Even more impressive is being able to move that much stuff that quickly without being a huge safety hazard in warehouse environments full of unpredictable humans. Although, that might become less of a problem over time, as other robots take over some of the tasks that humans have been doing. OTTO Motors and Clearpath Robotics have an ongoing partnership with Boston Dynamics, and we fully expect to see these AMRs hauling boxes for Stretch in the near future.

For a bit more, we spoke with OTTO CTO Ryan Gariepy via email.

IEEE Spectrum: What are the major differences between today’s OTTO 1500 and the one introduced six years ago, and why did you decide to make those changes?

Ryan Gariepy: Six years isn’t a long shelf life for an industrial product, but it’s a lifetime in the software world. We took the original OTTO 1500 and stripped it down to the chassis and drivetrain, and re-built it with more modern components (embedded controller, state-of-the-art sensors, next-generation lithium batteries, and more). But the biggest difference is in how we’ve integrated our autonomous software and our industrial safety systems. Our systems are safe throughout the entirety of the vehicle dynamics envelope from straight line motion to aggressive turning at speed in tight spaces. It corners at 2m/s and has 60% more throughput. No “simple rectangular” footprints here! On top of this, the entire customization, development, and validation process is done in a way which respects that our integration partners need to be able to take advantage of these capabilities themselves without needing to become experts in vehicle dynamics. 

As for “why now,” we’ve always known that an ecosystem of new sensors and controllers was going to emerge as the world caught on to the potential of heavy-load AMRs. We wanted to give the industry some time to settle out—making sure we had reliable and low-cost 3D sensors, for example, or industrial grade fanless computers which can still mount a reasonable GPU, or modular battery systems which are now built-in view of new certifications requirements. And, possibly most importantly, partners who see the promise of the market enough to accommodate our feedback in their product roadmaps.

How has the reception differed from the original introduction of the OTTO 1500 and the new version?
 
That’s like asking the difference between the public reception to the introduction of the first iPod in 2001 and the first iPhone in 2007. When we introduced our first AMR, very few people had even heard of them, let alone purchased one before. We spent a great deal of time educating the market on the basic functionality of an AMR: What it is and how it works kind of stuff. Today’s buyers are way more sophisticated, experienced, and approach automation from a more strategic perspective. What was once a tactical purchase to plug a hole is now part of a larger automation initiative. And while the next generation of AMRs closely resemble the original models from the outside, the software functionality and integration capabilities are night and day.

What’s the most valuable lesson you’ve learned?

We knew that our customers needed incredible uptime: 365 days, 24/7 for 10 years is the typical expectation. Some of our competitors have AMRs working in facilities where they can go offline for a few minutes or a few hours without any significant repercussions to the workflow. That’s not the case with our customers, where any stoppage at any point means everything shuts down. And, of course, Murphy’s law all but guarantees that it shuts down at 4:00 a.m. on Saturday, Japan Standard Time. So the humbling lesson wasn’t knowing that our customers wanted maintenance service levels with virtually no down time, the humbling part was the degree of difficulty in building out a service organization as rapidly as we rolled out customer deployments. Every customer in a new geography needed a local service infrastructure as well. Finally, service doesn’t mean anything without spare parts availability, which brings with it customs and shipping challenges. And, of course, as a Canadian company, we need to build all of that international service and logistics infrastructure right from the beginning. Fortunately, the groundwork we’d laid with Clearpath Robotics served as a good foundation for this.

How were you able to develop a new product with COVID restrictions in place?

We knew we couldn’t take an entire OTTO 1500 and ship it to every engineer’s home that needed to work on one, so we came up with the next best thing. We call it a ‘wall-bot’ and it’s basically a deconstructed 1500 that our engineers can roll into their garage. We were pleasantly surprised with how effective this was, though it might be the heaviest dev kit in the robot world. 

Also don’t forget that much of robotics is software driven. Our software development life cycle had already had a strong focus on Gazebo-based simulation for years due to it being unfeasible to give every in-office developer a multi-ton loaded robot to play with, and we’d already had a redundant VPN setup for the office. Finally, we’ve always been a remote-work-friendly culture ever since we started adopting telepresence robots and default-on videoconferencing in the pre-OTTO days. In retrospect, it seems like the largest area of improvement for us for the future is how quickly we could get people good home office setups while amid a pandemic.

Over the last few weeks, we’ve posted several articles about the next generation of warehouse manipulation robots designed to handle the non-stop stream of boxes that provide the foundation for modern ecommerce. But once these robots take boxes out of the back of a trailer or off of a pallet, there are yet more robots ready to autonomously continue the flow through a warehouse or distribution center. One of the beefiest of these autonomous mobile robots is the OTTO 1500, which is called the OTTO 1500 because (you guessed it) it can handle 1500 kg of cargo. Plus another 400kg of cargo, for a total of 1900 kg of cargo. Yeah, I don’t get it either. Anyway, it’s undergone a major update, which is a good excuse for us to ask OTTO CTO Ryan Gariepy some questions about it.

The earlier version, also named OTTO 1500, has over a million hours of real-world operation, which is impressive. Even more impressive is being able to move that much stuff that quickly without being a huge safety hazard in warehouse environments full of unpredictable humans. Although, that might become less of a problem over time, as other robots take over some of the tasks that humans have been doing. OTTO Motors and Clearpath Robotics have an ongoing partnership with Boston Dynamics, and we fully expect to see these AMRs hauling boxes for Stretch in the near future.

For a bit more, we spoke with OTTO CTO Ryan Gariepy via email.

IEEE Spectrum: What are the major differences between today’s OTTO 1500 and the one introduced six years ago, and why did you decide to make those changes?

Ryan Gariepy: Six years isn’t a long shelf life for an industrial product, but it’s a lifetime in the software world. We took the original OTTO 1500 and stripped it down to the chassis and drivetrain, and re-built it with more modern components (embedded controller, state-of-the-art sensors, next-generation lithium batteries, and more). But the biggest difference is in how we’ve integrated our autonomous software and our industrial safety systems. Our systems are safe throughout the entirety of the vehicle dynamics envelope from straight line motion to aggressive turning at speed in tight spaces. It corners at 2m/s and has 60% more throughput. No “simple rectangular” footprints here! On top of this, the entire customization, development, and validation process is done in a way which respects that our integration partners need to be able to take advantage of these capabilities themselves without needing to become experts in vehicle dynamics. 

As for “why now,” we’ve always known that an ecosystem of new sensors and controllers was going to emerge as the world caught on to the potential of heavy-load AMRs. We wanted to give the industry some time to settle out—making sure we had reliable and low-cost 3D sensors, for example, or industrial grade fanless computers which can still mount a reasonable GPU, or modular battery systems which are now built-in view of new certifications requirements. And, possibly most importantly, partners who see the promise of the market enough to accommodate our feedback in their product roadmaps.

How has the reception differed from the original introduction of the OTTO 1500 and the new version?
 
That’s like asking the difference between the public reception to the introduction of the first iPod in 2001 and the first iPhone in 2007. When we introduced our first AMR, very few people had even heard of them, let alone purchased one before. We spent a great deal of time educating the market on the basic functionality of an AMR: What it is and how it works kind of stuff. Today’s buyers are way more sophisticated, experienced, and approach automation from a more strategic perspective. What was once a tactical purchase to plug a hole is now part of a larger automation initiative. And while the next generation of AMRs closely resemble the original models from the outside, the software functionality and integration capabilities are night and day.

What’s the most valuable lesson you’ve learned?

We knew that our customers needed incredible uptime: 365 days, 24/7 for 10 years is the typical expectation. Some of our competitors have AMRs working in facilities where they can go offline for a few minutes or a few hours without any significant repercussions to the workflow. That’s not the case with our customers, where any stoppage at any point means everything shuts down. And, of course, Murphy’s law all but guarantees that it shuts down at 4:00 a.m. on Saturday, Japan Standard Time. So the humbling lesson wasn’t knowing that our customers wanted maintenance service levels with virtually no down time, the humbling part was the degree of difficulty in building out a service organization as rapidly as we rolled out customer deployments. Every customer in a new geography needed a local service infrastructure as well. Finally, service doesn’t mean anything without spare parts availability, which brings with it customs and shipping challenges. And, of course, as a Canadian company, we need to build all of that international service and logistics infrastructure right from the beginning. Fortunately, the groundwork we’d laid with Clearpath Robotics served as a good foundation for this.

How were you able to develop a new product with COVID restrictions in place?

We knew we couldn’t take an entire OTTO 1500 and ship it to every engineer’s home that needed to work on one, so we came up with the next best thing. We call it a ‘wall-bot’ and it’s basically a deconstructed 1500 that our engineers can roll into their garage. We were pleasantly surprised with how effective this was, though it might be the heaviest dev kit in the robot world. 

Also don’t forget that much of robotics is software driven. Our software development life cycle had already had a strong focus on Gazebo-based simulation for years due to it being unfeasible to give every in-office developer a multi-ton loaded robot to play with, and we’d already had a redundant VPN setup for the office. Finally, we’ve always been a remote-work-friendly culture ever since we started adopting telepresence robots and default-on videoconferencing in the pre-OTTO days. In retrospect, it seems like the largest area of improvement for us for the future is how quickly we could get people good home office setups while amid a pandemic.

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The machine learning industry’s efforts to measure itself using a standard yardstick has reached a milestone. Forgive the mixed metaphor, but that’s actually what’s happened with the release of MLPerf Inference v1.0 today. Using a suite of benchmark neural networks measured under a standardized set of conditions, 1,994 AI systems battled it out to show how quickly their neural networks can process new data. Separately, MLPerf tested an energy efficiency benchmark, with some 850 entrants for that.

This contest was the first following a set of trial runs where the AI consortium MLPerf and its parent organization MLCommons worked out the best measurement criteria. But the big winner in this first official version was the same as it had been in those warm-up rounds—Nvidia.

Entries were combinations of software and systems that ranged in scale from Raspberry Pis to supercomputers. They were powered by processors and accelerator chips from AMD, Arm, Centaur Technology, Edgecortix, Intel, Nvidia, Qualcomm, and Xilininx. And entries came from 17 organizations including Alibaba, Centaur, Dell Fujitsu, Gigabyte, HPE, Inspur, Krai, Lenovo, Moblint, Neuchips, and Supermicro.

Despite that diversity most of the systems used Nvidia GPUs to accelerate their AI functions. There were some other AI accelerators on offer, notably Qualcomm’s AI 100 and Edgecortix’s DNA. But Edgecortix was the only one of the many, many AI accelerator startups to jump in. And Intel chose to show off how well its CPUs did instead of offering up something from its US $2-billion acquisition of AI hardware startup Habana.

Before we get into the details of whose what was how fast, you’re going to need some background on how these benchmarks work. MLPerf is nothing like the famously straightforward Top500 list of the supercomputing great and good, where a single value can tell you most of what you need to know. The consortium decided that the demands of machine learning is just too diverse to be boiled down to something like tera-operations per watt, a metric often cited in AI accelerator research.

First, systems were judged on six neural networks. Entrants did not have to compete on all six, however.

  • BERT, for Bi-directional Encoder Representation from Transformers, is a natural language processing AI contributed by Google. Given a question input, BERT predicts a suitable answer.
  • DLRM, for Deep Learning Recommendation Model is a recommender system that is trained to optimize click-through rates. It’s used to recommend items for online shopping and rank search results and social media content. Facebook was the major contributor of the DLRM code.
  • 3D U-Net is used in medical imaging systems to tell which 3D voxel in an MRI scan are parts of a tumor and which are healthy tissue. It’s trained on a dataset of brain tumors.
  • RNN-T, for Recurrent Neural Network Transducer, is a speech recognition model. Given a sequence of speech input, it predicts the corresponding text.
  • ResNet is the granddaddy of image classification algorithms. This round used ResNet-50 version 1.5.
  • SSD, for Single Shot Detector, spots multiple objects within an image. It’s the kind of thing a self-driving car would use to find important things like other cars. This was done using either MobileNet version 1 or ResNet-34 depending on the scale of the system.

Competitors were divided into systems meant to run in a datacenter and those designed for operation at the “edge”—in a store, embedded in a security camera, etc.

Datacenter entrants were tested under two conditions. The first was a situation, called “offline”, where all the data was available in a single database, so the system could just hoover it up as fast as it could handle. The second more closely simulated the real life of a datacenter server, where data arrives in bursts and the system has to be able to complete its work quickly and accurately enough to handle the next burst.

Edge entrants tackled the offline scenario as well. But they also had to handle a test where they are fed a single stream of data, say a single conversation for language processing, and a multistream situation like a self-driving car might have to deal with from its multiple cameras.

Got all that? No? Well, Nvidia summed it up in this handy slide:

Image: NVIDIA

And finally, the efficiency benchmarks were done by measuring the power draw at the wall plug and averaged over 10 minutes to smooth out the highs-and-lows caused by processors scaling their voltages and frequencies.

Here, then, are the tops for each category:

FASTEST

Datacenter (commercially available systems, ranked by server condition)

Image Classification Object Detection Medical Imaging Speech-to-Text Natural Language Processing Recommendation Submitter Inspur DellEMC NVIDIA DellEMC DellEMC Inspur System name NF5488A5 Dell EMC DSS 8440 (10x A100-PCIe-40GB) NVIDIA DGX-A100 (8x A100-SXM-80GB, TensorRT) Dell EMC DSS 8440 (10x A100-PCIe-40GB) Dell EMC DSS 8440 (10x A100-PCIe-40GB) NF5488A5 Processor AMD EPYC 7742 Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz AMD EPYC 7742 Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz AMD EPYC 7742 No. Processors 2 2 2 2 2 2 Accelerator NVIDIA A100-SXM-80GB NVIDIA A100-PCIe-40GB NVIDIA A100-SXM-80GB NVIDIA A100-PCIe-40GB NVIDIA A100-PCIe-40GB NVIDIA A100-SXM-80GB No. Accelerators 8 10 8 10 10 8 Server queries/s 271,246 8,265 479.65 107,987 26,749 2,432,860 Offline samples/s 307,252 7,612 479.65 107,269 29,265 2,455,010

Edge (commercially available, ranked by single-stream latency)

Image Classification Object Detection (small) Object Detection (large) Medical Imaging Speech-to-Text Natural Language Processing Submitter NVIDIA NVIDIA NVIDIA NVIDIA NVIDIA NVIDIA System name NVIDIA DGX-A100 (1x A100-SXM-80GB, TensorRT, Triton) NVIDIA DGX-A100 (1x A100-SXM-80GB, TensorRT, Triton) NVIDIA DGX-A100 (1x A100-SXM-80GB, TensorRT, Triton) NVIDIA DGX-A100 (1x A100-SXM-80GB, TensorRT) NVIDIA DGX-A100 (1x A100-SXM-80GB, TensorRT) NVIDIA DGX-A100 (1x A100-SXM-80GB, TensorRT) Processor AMD EPYC 7742 AMD EPYC 7742 AMD EPYC 7742 AMD EPYC 7742 AMD EPYC 7742 AMD EPYC 7742 No. Processors 2 2 2 2 2 2 Accelerator NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB No. Accelerators 1 1 1 1 1 1 Single stream latency (milliseconds) 0.431369 0.25581 1.686353 19.919082 22.585203 1.708807 Multiple stream (streams) 1344 1920 56 Offline samples/s 38011.6 50926.6 985.518 60.6073 14007.6 3601.96

The Most Efficient

Datacenter

Image Classification Object Detection Medical Imaging Speech-to-Text Natural Language Processing Recommendation Submitter Qualcomm Qualcomm NVIDIA NVIDIA NVIDIA NVIDIA System name Gigabyte R282-Z93 5x QAIC100 Gigabyte R282-Z93 5x QAIC100 Gigabyte G482-Z54 (8x A100-PCIe, MaxQ, TensorRT) NVIDIA DGX Station A100 (4x A100-SXM-80GB, MaxQ, TensorRT) NVIDIA DGX Station A100 (4x A100-SXM-80GB, MaxQ, TensorRT) NVIDIA DGX Station A100 (4x A100-SXM-80GB, MaxQ, TensorRT) Processor AMD EPYC 7282 16-Core Processor AMD EPYC 7282 16-Core Processor AMD EPYC 7742 AMD EPYC 7742 AMD EPYC 7742 AMD EPYC 7742 No. Processors 2 2 2 1 1 1 Accelerator QUALCOMM Cloud AI 100 PCIe HHHL QUALCOMM Cloud AI 100 PCIe HHHL NVIDIA A100-PCIe-40GB NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB NVIDIA A100-SXM-80GB No. Accelerators 5 5 8 4 4 4 Server queries/s 78,502 1557 372 43,389 10,203 890,334 System Power (Watts) 534 548 2261 1314 1302 1342 Queries/joule 147.06 2.83 0.16 33.03 7.83 663.61

Edge (commercially available, ranked by single-stream latency)

Image Classification Object Detection (large) Object Detection (small) Medical Imaging Speech-to-Text Natural Language Processing Submitter Qualcomm NVIDIA Qualcomm NVIDIA NVIDIA NVIDIA System name AI Development Kit NVIDIA Jetson Xavier NX (MaxQ, TensorRT) AI Development Kit NVIDIA Jetson Xavier NX (MaxQ, TensorRT) NVIDIA Jetson Xavier NX (MaxQ, TensorRT) NVIDIA Jetson Xavier NX (MaxQ, TensorRT) Processor Qualcomm Snapdragon 865 NVIDIA Carmel (ARMv8.2) Qualcomm Snapdragon 865 NVIDIA Carmel (ARMv8.2) NVIDIA Carmel (ARMv8.2) NVIDIA Carmel (ARMv8.2) No. Processors 1 1 1 1 1 1 Accelerator QUALCOMM Cloud AI 100 DM.2e NVIDIA Xavier NX QUALCOMM Cloud AI 100 DM.2 NVIDIA Xavier NX NVIDIA Xavier NX NVIDIA Xavier NX No. Accelerators 1 1 1 1 1 1 Single stream latency 0.85 1.67 30.44 819.08 372.37 57.54 System energy/stream (joules) 0.02 0.02 0.60 12.14 3.45 0.59

The continuing lack of entrants from AI hardware startups is glaring at this point, especially considering that many of them are members of MLCommons. When I’ve asked certain startups about it, they usually answer that the best measure of their hardware is how it runs their potential customers’ specific neural networks rather than how well they do on benchmarks.

That seems fair, of course, assuming these startups can get the attention of potential customers in the first place. It also assumes that customers actually know what they need.

“If you’ve never done AI, you don’t know what to expect; you don’t know what performance you want to hit; you don’t know what combinations you want with CPUs, GPUs, and accelerators,” says Armando Acosta, product manager for AI, high-performance computing, and data analytics at Dell Technologies. MLPerf, he says, “really gives customers a good baseline.”

Due to author error a mixed metaphor was labelled as a pun in an earlier version of this post.

Many species of termites build large, structurally complex mounds, and the mechanisms behind this coordinated construction have been a longstanding topic of investigation. Recent work has suggested that humidity may play a key role in the mound expansion of savannah-dwelling Macrotermes species: termites preferentially deposit soil on the mound surface at the boundary of the high-humidity region characteristic of the mound interior, implying a coordination mechanism through environmental feedback where addition of wet soil influences the humidity profile and vice versa. Here we test this potential mechanism physically using a robotic system. Local humidity measurements provide a cue for material deposition. As the analogue of the termite's deposition of wet soil and corresponding local increase in humidity, the robot drips water onto an absorbent substrate as it moves. Results show that the robot extends a semi-enclosed area outward when air is undisturbed, but closes it off when air is disturbed by an external fan, consistent with termite building activity in still vs. windy conditions. This result demonstrates an example of adaptive construction patterns arising from the proposed coordination mechanism, and supports the hypothesis that such a mechanism operates in termites.

The dielectric elastomer (DE) is a new kind of functional polymer that can be used as a smart actuator due to the large deformation induced by voltage excitation. Dielectric elastomer actuators (DEAs) are usually excited by dynamic voltages to generate alternating motions. DEAs are prone to premature breakdown failure during the dynamic excitation, while the research on the breakdown of DEAs under cyclic voltage excitation is still not fully revealed. In this paper, the dynamic breakdown behaviors of DEAs made from VHB4910 film were experimentally investigated. The factors affecting the breakdown behavior of DEAs under dynamic voltages were determined, and the relevant changing laws were summarized accordingly. The experimental results show that under dynamic voltage excitation, the critical breakdown voltage of DEAs were augmented slowly with voltage frequency and showed a substantial dispersion. In addition, the maximum cycle numbers before breakdown were significantly affected by voltage parameters (such as frequency, amplitude, waveform). Finally, the underlying mechanisms of breakdown under cyclic voltages were discussed qualitatively, a power-law equation was proposed to characterize the maximum cycle number for the dynamic breakdown of DEAs, and related parameters were fitted. This study provides a new path to predict the service life of DEAs under dynamic voltage.

Kate Darling is an expert on human robot interaction, robot ethics, intellectual property, and all sorts of other things at the MIT Media Lab. She’s written several excellent articles for us in the past, and we’re delighted to be able to share this excerpt from her new book, which comes out today. Entitled The New Breed: What Our History with Animals Reveals about Our Future with Robots, Kate’s book is an exploration of how animals can help us understand our robot relationships, and how far that comparison can really be extended. It’s solidly based on well-cited research, including many HRI studies that we’ve written about in the past, but Kate brings everything together and tells us what it all could mean as robots continue to integrate themselves into our lives. 

The following excerpt is The Power of Movement, a section from the chapter Robots Versus Toasters, which features one of the saddest robot videos I’ve ever seen, even after nearly a decade. Enjoy!

When the first black-and-white motion pictures came to the screen, an 1896 film showing in a Paris cinema is said to have caused a stampede: the first-time moviegoers, watching a giant train barrel toward them, jumped out of their seats and ran away from the screen in panic. According to film scholar Martin Loiperdinger, this story is no more than an urban legend. But this new media format, “moving pictures,” proved to be both immersive and compelling, and was here to stay. Thanks to a baked-in ability to interpret motion, we’re fascinated even by very simple animation because it tells stories we intuitively understand.

In a seminal study from the 1940s, psychologists Fritz Heider and Marianne Simmel showed participants a black-and-white movie of simple, geometrical shapes moving around on a screen. When instructed to describe what they were seeing, nearly every single one of their participants interpreted the shapes to be moving around with agency and purpose. They described the behavior of the triangles and circle the way we describe people’s behavior, by assuming intent and motives. Many of them went so far as to create a complex narrative around the moving shapes. According to one participant: “A man has planned to meet a girl and the girl comes along with another man. [ . . . ] The girl gets worried and races from one corner to the other in the far part of the room. [ . . . ] The girl gets out of the room in a sudden dash just as man number two gets the door open. The two chase around the outside of the room together, followed by man number one. But they finally elude him and get away. The first man goes back and tries to open his door, but he is so blinded by rage and frustration that he can not open it.”

What brought the shapes to life for Heider and Simmel’s participants was solely their movement. We can interpret certain movement in other entities as “worried,” “frustrated,” or “blinded by rage,” even when the “other” is a simple black triangle moving across a white background. A number of studies document how much information we can extract from very basic cues, getting us to assign emotions and gender identity to things as simple as moving points of light. And while we might not run away from a train on a screen, we’re still able to interpret the movement and may even get a little thrill from watching the train in a more modern 3D screening. (There are certainly some embarrassing videos of people—maybe even of me—when we first played games wearing virtual reality headsets.)

Many scientists believe that autonomous movement activates our “life detector.” Because we’ve evolved needing to quickly identify natural predators, our brains are on constant lookout for moving agents. In fact, our perception is so attuned to movement that we separate things into objects and agents, even if we’re looking at a still image. Researchers Joshua New, Leda Cosmides, and John Tooby showed people photos of a variety of scenes, like a nature landscape, a city scene, or an office desk. Then, they switched in an identical image with one addition; for example, a bird, a coffee mug, an elephant, a silo, or a vehicle. They measured how quickly the participants could identify the new appearance. People were substantially quicker and more accurate at detecting the animals compared to all of the other categories, including larger objects and vehicles.

The researchers also found evidence that animal detection activated an entirely different region of people’s brains. Research like this suggests that a specific part of our brain is constantly monitoring for lifelike animal movement. This study in particular also suggests that our ability to separate animals and objects is more likely to be driven by deep ancestral priorities than our own life experiences. Even though we have been living with cars for our whole lives, and they are now more dangerous to us than bears or tigers, we’re still much quicker to detect the presence of an animal.

The biological hardwiring that detects and interprets life in autonomous agent movement is even stronger when it has a body and is in the room with us. John Harris and Ehud Sharlin at the University of Calgary tested this projection with a moving stick. They took a long piece of wood, about the size of a twirler’s baton, and attached one end to a base with motors and eight degrees of freedom. This allowed the researchers to control the stick remotely and wave it around: fast, slow, doing figure eights, etc. They asked the experiment participants to spend some time alone in a room with the moving stick. Then, they had the participants describe their experience.

Only two of the thirty participants described the stick’s movement in technical terms. The others told the researchers that the stick was bowing or otherwise greeting them, claimed it was aggressive and trying to attack them, described it as pensive, “hiding something,” or even “purring happily.” At least ten people said the stick was “dancing.” One woman told the stick to stop pointing at her.

If people can imbue a moving stick with agency, what happens when they meet R2-D2? Given our social tendencies and ingrained responses to lifelike movement in our physical space, it’s fairly unsurprising that people perceive robots as being alive. Robots are physical objects in our space that often move in a way that seems (to our lizard brains) to have agency. A lot of the time, we don’t perceive robots as objects—to us, they are agents. And, while we may enjoy the concept of pet rocks, we love to anthropomorphize agent behavior even more.

We already have a slew of interesting research in this area. For example, people think a robot that’s present in a room with them is more enjoyable than the same robot on a screen and will follow its gaze, mimic its behavior, and be more willing to take the physical robot’s advice. We speak more to embodied robots, smile more, and are more likely to want to interact with them again. People are more willing to obey orders from a physical robot than a computer. When left alone in a room and given the opportunity to cheat on a game, people cheat less when a robot is with them. And children learn more from working with a robot compared to the same character on a screen. We are better at recognizing a robot’s emotional cues and empathize more with physical robots. When researchers told children to put a robot in a closet (while the robot protested and said it was afraid of the dark), many of the kids were hesitant. 

Even adults will hesitate to switch off or hit a robot, especially when they perceive it as intelligent. People are polite to robots and try to help them. People greet robots even if no greeting is required and are friendlier if a robot greets them first. People reciprocate when robots help them. And, like the socially inept [software office assistant] Clippy, when people don’t like a robot, they will call it names. What’s noteworthy in the context of our human comparison is that the robots don’t need to look anything like humans for this to happen. In fact, even very simple robots, when they move around with “purpose,” elicit an inordinate amount of projection from the humans they encounter. Take robot vacuum cleaners. By 2004, a million of them had been deployed and were sweeping through people’s homes, vacuuming dirt, entertaining cats, and occasionally getting stuck in shag rugs. The first versions of the disc-shaped devices had sensors to detect things like steep drop-offs, but for the most part they just bumbled around randomly, changing direction whenever they hit a wall or a chair.

iRobot, the company that makes the most popular version (the Roomba) soon noticed that their customers would send their vacuum cleaners in for repair with names (Dustin Bieber being one of my favorites). Some Roomba owners would talk about their robot as though it were a pet. People who sent in malfunctioning devices would complain about the company’s generous policy to offer them a brand-new replacement, demanding that they instead fix “Meryl Sweep” and send her back. The fact that the Roombas roamed around on their own lent them a social presence that people’s traditional, handheld vacuum cleaners lacked. People decorated them, talked to them, and felt bad for them when they got tangled in the curtains.

Tech journalists reported on the Roomba’s effect, calling robovacs “the new pet craze.” A 2007 study found that many people had a social relationship with their Roombas and would describe them in terms that evoked people or animals. Today, over 80 percent of Roombas have names. I don’t have access to naming statistics for the handheld Dyson vacuum cleaner, but I’m pretty sure the number is lower.

Robots are entering our lives in many shapes and forms, and even some of the most simple or mechanical robots can prompt a visceral response. And the design of robots isn’t likely to shift away from evoking our biological reactions—especially because some robots are designed to mimic lifelike movement on purpose.

Excerpted from THE NEW BREED: What Our History with Animals Reveals about Our Future with Robots by Kate Darling. Published by Henry Holt and Company. Copyright © 2021 by Kate Darling. All rights reserved.

Kate’s book is available today from Annie Bloom’s Books in SW Portland, Oregon. It’s also available from Powell’s Books, and if you don’t have the good fortune of living in Portland, you can find it in both print and digital formats pretty much everywhere else books are sold.

As for Robovie, the claustrophobic robot that kept getting shoved in a closet, we recently checked in with Peter Kahn, the researcher who created the experiment nearly a decade ago, to make sure that the poor robot ended up okay. “Robovie is doing well,” Khan told us. “He visited my lab on 2-3 other occasions and participated in other experiments. Now he’s back in Japan with the person who helped make him, and who cares a lot about him.” That person is Takayuki Kanda at ATR, who we’re happy to report is still working with Robovie in the context of human-robot interaction. Thanks Robovie! 

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