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We AI researchers are concerned about the potential impact of artificially intelligent systems on humanity. In the first half of this essay, I argue that ethics is an evolved body of cultural knowledge that (among other things) encourages individual behavior that promotes the welfare of the society (which in turn promotes the welfare of its individual members). The causal paths involved suggest that trust and cooperation play key roles in this process. In the second half of the essay, I consider whether the key role of trust exposes our society to existential threats. This possibility arises because decision-making agents (humans, AIs, and others) necessarily rely on simplified models to cope with the unbounded complexity of our physical and social world. By selecting actions to maximize a utility measure, a well-formulated game theory model can be a powerful and valuable tool. However, a poorly-formulated game theory model may be uniquely harmful, in cases where the action it recommends deliberately exploits the vulnerability and violates the trust of cooperative partners. Widespread use of such models can erode the overall levels of trust in the society. Cooperation is reduced, resources are constrained, and there is less ability to meet challenges or take advantage of opportunities. Loss of trust will affect humanity’s ability to respond to existential threats such as climate change.

Introduction: Many employees report high physical strain from overhead work and resulting musculoskeletal disorders. The consequences of these conditions extend far beyond everyday working life and can severely limit the quality of life of those affected. One solution to this problem may be the use of upper-limb exoskeletons, which are supposed to relieve the shoulder joint in particular. The aim of this literature review was to provide an overview of the use and efficacy of exoskeletons for upper extremities in the working environment.

Methods: A literature review was conducted using the PICO scheme and the PRISMA statement. To this end, a systematic search was performed in the PubMed, Web of Science and Scopus databases in May 2020 and updated in February 2022. The obtained studies were screened using previously defined inclusion and exclusion criteria and assessed for quality. Pertinent data were then extracted from the publications and analyzed with regard to type of exoskeleton used as well as efficacy of exoskeleton use.

Results: 35 suitable studies were included in the review. 18 different exoskeletons were examined. The majority of the exoskeletons only supported the shoulder joint and were used to assist individuals working at or above shoulder level. The main focus of the studies was the reduction of muscle activity in the shoulder area. Indeed, 16 studies showed a reduced activity in the deltoid and trapezius muscles after exoskeleton use. Kinematically, a deviation of the movement behavior could be determined in some models. In addition, study participants reported perceived reduction in exertion and discomfort.

Discussion: Exoskeletons for upper extremities may generate significant relief for the intended tasks, but the effects in the field (i.e., working environment) are less pronounced than in the laboratory setting. This may be due to the fact that not only overhead tasks but also secondary tasks have to be performed in the field. In addition, currently available exoskeletons do not seem to be suitable for all overhead workplaces and should always be assessed in the human-workplace context. Further studies in various settings are required that should also include more females and older people.



Biological actuators can be fantastically complex. Networks of nerves can drive tiny muscles to make coordinated motions in ways that are very difficult for engineered systems to match. You can see how far robotics is from nature when you look at our best attempts to mimic things like snakes or millipedes or even intestines, all of which function based on lots of tiny muscles working together. Usually, the closest that robots can get to nature is by connecting a bunch of chonky discrete actuators together, which sometimes results in similar functionality but with far less efficiency and elegance.

What these biological systems have in common is peristalsis—a series of wavelike coordinated muscle contractions that animals use to move themselves, and that animal insides use to move stuff around. Take just one solitary wave and you’ve got a soliton, which should probably not be used to propel starships at faster-than-light speeds. Solitons have been mimicked by robotic systems before, although by (again) relying on a complicated and expensive chain of individual actuators.

A recent paper from the Pikul group at the University of Pennsylvania is exploring a much simpler approach to generating soliton waves: dominoes.

Falling dominoes exhibit soliton behavior as they topple in sequence, and you can propagate the wave with a single actuator at the first domino. Using some of those fancy “cheating” dominoes that are hinged at the bottom allows a second actuator to send the soliton right back again, resetting the system. “We wanted to realize a system that would be low power, simple, and could scale to smaller robots,” Penn’s James Pikul tells IEEE Spectrum. “Yichao Shi, the first author, was the one who connected the elegant movement of dominoes as a path towards realizing a wavelike motion in a robotic actuator. As we played with the form factors of the dominoes, we were surprised by how complex the wave shape can be programmed, yet how simply the system is actuated.”

Programming a wave shape with dominoes involves adjusting their physical parameters, like domino height and how close each domino is to its neighbor. Depending on what you want your domino actuator to do, the wave shape is easily adjustable at fabrication time, and the researchers performed a series of practical(ish) experiments to see how effective their actuator could be. First, a sort of conveyor that can push objects in front of the soliton to move them along arbitrary paths:

In addition to moving objects with soliton waves, they’re also used in nature by animals for motion, so the researchers built themselves a soliton-powered mantis shrimp robot that uses cascading domino “fins” (that actually look kind of similar to the fins on the animal) combined with footlike things to provide anisotropic friction that results in forward propulsion:

For comparison, here’s a picture of a real mantis shrimp; see if you can spot the soliton wave:

Evan Ackerman

Cool, right?

For more details, we interviewed James Pikul and Yichao Shi via email.

How does the peristaltic motion of the actuator you’ve developed differ from biological models?

James H. Pikul & Yichao Shi: Since our synthetic muscles are less powerful and efficient than animal muscles, we didn’t see coordinated actuation of multiple individual synthetic muscles as an effective way to realize our goals. So, in our system, all the dominoes are linked and constrained by mechanical contact so that they create a soliton with a single signal that is sent to the boundary domino. The advantage of this approach, where only the boundaries have actuators, is that the system is easier to control and build than systems that more precisely mimic biology, and the mechanical constraints make the cascading dominoes easy to apply to different scales. However, the lower degrees of freedom also mean the dominoes have limits in how robust and agile they can be. It is difficult, for example, to push multiple objects through the cascading dominoes at once, or to push liquids. In addition, the dominoes are hard, and although they can be made flexible, the synthetic system is not as compliant as the natural systems.

Can you elaborate on some of the advantages of a cascading domino actuator relative to other common robotic methods of moving objects, like belt conveyors or roller conveyors?

Pikul/Shi: Common automated conveyor systems usually require multiple actuators and separate segments to achieve nonlinear delivery paths. In comparison, our cascading dominoes can realize complex paths with both curvature and elevation changes while being actuated by a single actuator. The cascading dominoes can also be lightweight, flexible, and actuated by a variety of soft actuators (for example, shape memory alloys, inflatable actuators, bending actuators, etc.), whereas conveyor systems are operated by electric motors.

Are there different tasks that the different wave shapes could be optimized for?

Pikul/Shi: In our paper, we show how the force, speed, and aspect ratio (or quality factor) of the wave can be adjusted with small changes in geometry. Having a wave that accelerates at the end of motion, instead of having a uniform wave speed, could be better for launching a projectile or jumping, whereas a uniform wave speed is better for maintaining a constant walking speed. In addition, the aspect ratio of the wave can be tuned for the type of object that is being moved or even for the type of terrain that the robot is moving across. All of these options allow for tunability, which is critical for many robotic designers.

Wave propagation can be used for a wide variety of applications beyond moving objects. One interesting application could be swimming robots, because many aquatic organisms, from flagella in bacteria to eel and tuna swimming, flex their bodies in a wave to displace water and move forward. Modifying the wave shape could also change the way energy or information is transferred through mechanical systems, which could be useful for mechanical metamaterials.

What kinds of potential applications do you think this research could eventually be applied toward in a useful and practical way?

Pikul/Shi: As we have demonstrated in the paper, the cascading dominoes show effective movement of objects, even with multiple objects or through a complex path, and they can provide a simple tool for locomotion. We think this will be most useful in applications that benefit from low cost or disposable systems, such as swarm robots or single-use robots, or applications that cannot accommodate the complexity of many actuators, such as small-scale robots or mass-produced products, such as toys. In addition to wave propagation, our cascading dominoes provide an alternative to classic linkages or gears for force transmission.

What are you working on next?

Pikul/Shi: Our ambition is to develop an “organ” for robots that allow them to eat objects in the environment and electrochemically convert the chemical energy of those objects into usable electrical power. Realizing these cascading dominoes is a first step in this direction because it allows us to transport food through an internal synthetic digestive system.

We did ask for some extra detail on that last bit, and we’re relieved to be able to report that the “objects” in question are metallic, so it might be a little less questionable to say that the robot is consuming local resources rather than eating in an organic way. This is based on recent research the Pikul group has done on metal-eating robots, and not on this scary DARPA project. Phew!

Harnessing Cascading Dominoes for Peristaltic Wave Motion,” by Yichao Shi, Zhimin Jiang, and James H. Pikul from the University of Pennsylvania, is published in IEEE Robotics and Automation Letters.



Biological actuators can be fantastically complex. Networks of nerves can drive tiny muscles to make coordinated motions in ways that are very difficult for engineered systems to match. You can see how far robotics is from nature when you look at our best attempts to mimic things like snakes or millipedes or even intestines, all of which function based on lots of tiny muscles working together. Usually, the closest that robots can get to nature is by connecting a bunch of chonky discrete actuators together, which sometimes results in similar functionality but with far less efficiency and elegance.

What these biological systems have in common is peristalsis—a series of wavelike coordinated muscle contractions that animals use to move themselves, and that animal insides use to move stuff around. Take just one solitary wave and you’ve got a soliton, which should probably not be used to propel starships at faster-than-light speeds. Solitons have been mimicked by robotic systems before, although by (again) relying on a complicated and expensive chain of individual actuators.

A recent paper from the Pikul group at the University of Pennsylvania is exploring a much simpler approach to generating soliton waves: dominoes.

Falling dominoes exhibit soliton behavior as they topple in sequence, and you can propagate the wave with a single actuator at the first domino. Using some of those fancy “cheating” dominoes that are hinged at the bottom allows a second actuator to send the soliton right back again, resetting the system. “We wanted to realize a system that would be low power, simple, and could scale to smaller robots,” Penn’s James Pikul tells IEEE Spectrum. “Yichao Shi, the first author, was the one who connected the elegant movement of dominoes as a path towards realizing a wavelike motion in a robotic actuator. As we played with the form factors of the dominoes, we were surprised by how complex the wave shape can be programmed, yet how simply the system is actuated.”

Programming a wave shape with dominoes involves adjusting their physical parameters, like domino height and how close each domino is to its neighbor. Depending on what you want your domino actuator to do, the wave shape is easily adjustable at fabrication time, and the researchers performed a series of practical(ish) experiments to see how effective their actuator could be. First, a sort of conveyor that can push objects in front of the soliton to move them along arbitrary paths:

In addition to moving objects with soliton waves, they’re also used in nature by animals for motion, so the researchers built themselves a soliton-powered mantis shrimp robot that uses cascading domino “fins” (that actually look kind of similar to the fins on the animal) combined with footlike things to provide anisotropic friction that results in forward propulsion:

For comparison, here’s a picture of a real mantis shrimp; see if you can spot the soliton wave:

Evan Ackerman

Cool, right?

For more details, we interviewed James Pikul and Yichao Shi via email.

How does the peristaltic motion of the actuator you’ve developed differ from biological models?

James H. Pikul & Yichao Shi: Since our synthetic muscles are less powerful and efficient than animal muscles, we didn’t see coordinated actuation of multiple individual synthetic muscles as an effective way to realize our goals. So, in our system, all the dominoes are linked and constrained by mechanical contact so that they create a soliton with a single signal that is sent to the boundary domino. The advantage of this approach, where only the boundaries have actuators, is that the system is easier to control and build than systems that more precisely mimic biology, and the mechanical constraints make the cascading dominoes easy to apply to different scales. However, the lower degrees of freedom also mean the dominoes have limits in how robust and agile they can be. It is difficult, for example, to push multiple objects through the cascading dominoes at once, or to push liquids. In addition, the dominoes are hard, and although they can be made flexible, the synthetic system is not as compliant as the natural systems.

Can you elaborate on some of the advantages of a cascading domino actuator relative to other common robotic methods of moving objects, like belt conveyors or roller conveyors?

Pikul/Shi: Common automated conveyor systems usually require multiple actuators and separate segments to achieve nonlinear delivery paths. In comparison, our cascading dominoes can realize complex paths with both curvature and elevation changes while being actuated by a single actuator. The cascading dominoes can also be lightweight, flexible, and actuated by a variety of soft actuators (for example, shape memory alloys, inflatable actuators, bending actuators, etc.), whereas conveyor systems are operated by electric motors.

Are there different tasks that the different wave shapes could be optimized for?

Pikul/Shi: In our paper, we show how the force, speed, and aspect ratio (or quality factor) of the wave can be adjusted with small changes in geometry. Having a wave that accelerates at the end of motion, instead of having a uniform wave speed, could be better for launching a projectile or jumping, whereas a uniform wave speed is better for maintaining a constant walking speed. In addition, the aspect ratio of the wave can be tuned for the type of object that is being moved or even for the type of terrain that the robot is moving across. All of these options allow for tunability, which is critical for many robotic designers.

Wave propagation can be used for a wide variety of applications beyond moving objects. One interesting application could be swimming robots, because many aquatic organisms, from flagella in bacteria to eel and tuna swimming, flex their bodies in a wave to displace water and move forward. Modifying the wave shape could also change the way energy or information is transferred through mechanical systems, which could be useful for mechanical metamaterials.

What kinds of potential applications do you think this research could eventually be applied toward in a useful and practical way?

Pikul/Shi: As we have demonstrated in the paper, the cascading dominoes show effective movement of objects, even with multiple objects or through a complex path, and they can provide a simple tool for locomotion. We think this will be most useful in applications that benefit from low cost or disposable systems, such as swarm robots or single-use robots, or applications that cannot accommodate the complexity of many actuators, such as small-scale robots or mass-produced products, such as toys. In addition to wave propagation, our cascading dominoes provide an alternative to classic linkages or gears for force transmission.

What are you working on next?

Pikul/Shi: Our ambition is to develop an “organ” for robots that allow them to eat objects in the environment and electrochemically convert the chemical energy of those objects into usable electrical power. Realizing these cascading dominoes is a first step in this direction because it allows us to transport food through an internal synthetic digestive system.

We did ask for some extra detail on that last bit, and we’re relieved to be able to report that the “objects” in question are metallic, so it might be a little less questionable to say that the robot is consuming local resources rather than eating in an organic way. This is based on recent research the Pikul group has done on metal-eating robots, and not on this scary DARPA project. Phew!

Harnessing Cascading Dominoes for Peristaltic Wave Motion,” by Yichao Shi, Zhimin Jiang, and James H. Pikul from the University of Pennsylvania, is published in IEEE Robotics and Automation Letters.

Soft robots are typically intended to operate in highly unpredictable and unstructured environments. Although their soft bodies help them to passively conform to their environment, the execution of specific tasks within such environments often requires the help of an operator that supervises the interaction between the robot and its environment and adjusts the actuation inputs in order to successfully execute the task. However, direct observation of the soft robot is often impeded by the environment in which it operates. Therefore, the operator has to depend on a real-time simulation of the soft robot based on the signals from proprioceptive sensors. However, the complicated three-dimensional (3D) configurations of the soft robot can be difficult to interpret using traditional visualization techniques. In this work, we present an open-source framework for real-time 3D reconstruction of soft robots in eXtended Reality (Augmented and Virtual Reality), based on signals from their proprioceptive sensors. This framework has a Robot Operating System (ROS) backbone, allowing for easy integration with existing soft robot control algorithms for intuitive and real-time teleoperation. This approach is demonstrated in Augmented Reality using a Microsoft Hololens device and runs at up to 60 FPS. We explore the influence that system parameters such as mesh density and armature complexity have on the reconstruction's key performance metrics (i.e., speed, scalability). The open-source framework is expected to function as a platform for future research and developments on real-time remote control of soft robots operating in environments that impede direct observation of the robot.

Honey bees live in colonies of thousands of individuals, that not only need to collaborate with each other but also to interact intensively with their ecosystem. A small group of robots operating in a honey bee colony and interacting with the queen bee, a central colony element, has the potential to change the collective behavior of the entire colony and thus also improve its interaction with the surrounding ecosystem. Such a system can be used to study and understand many elements of bee behavior within hives that have not been adequately researched. We discuss here the applicability of this technology for ecosystem protection: A novel paradigm of a minimally invasive form of conservation through “Ecosystem Hacking”. We discuss the necessary requirements for such technology and show experimental data on the dynamics of the natural queen’s court, initial designs of biomimetic robotic surrogates of court bees, and a multi-agent model of the queen bee court system. Our model is intended to serve as an AI-enhanceable coordination software for future robotic court bee surrogates and as a hardware controller for generating nature-like behavior patterns for such a robotic ensemble. It is the first step towards a team of robots working in a bio-compatible way to study honey bees and to increase their pollination performance, thus achieving a stabilizing effect at the ecosystem level.

An explicit mathematical form of a human’s step-and-brake controller is identified through motion measurement of the human subject. The controller was originally designed for biped robots based on the reduced-order dynamics and the model predictive control scheme with the terminal capturability condition, and is compatible with both stand-still and stepping motions. The minimal number of parameters facilitates the identification from measured trajectories of the center of mass and the zero-moment point of the human subject. In spite of the minimality, the result only suited the human’s behaviors well with slight modifications of the model by taking direction-dependency of the natural falling speed and the inertial torque about the center of mass into account. Furthermore, the parameters are successfully identified even from the first half of motion sequence, which means that the proposed method is available in designing on-the-fly systems to evaluate balancing abilities of humans and to assist balances of humans in walking.

Leg motion is essential to everyday tasks, yet many face a daily struggle due to leg motion impairment. Traditional robotic solutions for lower limb rehabilitation have arisen, but they may bare some limitations due to their cost. Soft robotics utilizes soft, pliable materials which may afford a less costly robotic solution. This work presents a soft-pneumatic-actuator-driven exoskeleton for hip flexion rehabilitation. An array of soft pneumatic rotary actuators is used for torque generation. An analytical model of the actuators is validated and used to determine actuator parameters for the target application of hip flexion. The performance of the assembly is assessed, and it is found capable of the target torque for hip flexion, 19.8 Nm at 30°, requiring 86 kPa to reach that torque output. The assembly exhibits a maximum torque of 31 Nm under the conditions tested. The full exoskeleton assembly is then assessed with healthy human subjects as they perform a set of lower limb motions. For one motion, the Leg Raise, a muscle signal reduction of 43.5% is observed during device assistance, as compared to not wearing the device. This reduction in muscle effort indicates that the device is effective in providing hip flexion assistance and suggests that pneumatic-rotary-actuator-driven exoskeletons are a viable solution to realize more accessible options for those who suffer from lower limb immobility.

Modern industrial robots are increasingly deployed in dynamic environments, where unpredictable events are expected to impact the robot’s operation. Under these conditions, runtime task replanning is required to avoid failures and unnecessary stops, while keeping up productivity. Task replanning is a long-sighted complement to path replanning, which is mostly concerned with avoiding unexpected obstacles that can lead to potentially unsafe situations. This paper focuses on task replanning as a way to dynamically adjust the robot behaviour to the continuously evolving environment in which it is deployed. Analogously to probabilistic roadmaps used in path planning, we propose the concept of Task roadmaps as a method to replan tasks by leveraging an offline generated search space. A graph-based model of the robot application is converted to a task scheduling problem to be solved by a proposed Branch and Bound (B&B) approach and two benchmark approaches: Mixed Integer Linear Programming (MILP) and Planning Domain Definition Language (PDDL). The B&B approach is proposed to compute the task roadmap, which is then reused to replan for unforeseeable events. The optimality and efficiency of this replanning approach are demonstrated in a simulation-based experiment with a mobile manipulator in a kitting application. In this study, the proposed B&B Task Roadmap replanning approach is significantly faster than a MILP solver and a PDDL based planner.

This paper addresses the general problem of deformable linear object manipulation. The main application we consider is in the field of agriculture, for plant grasping, but may have interests in other tasks such as human daily activities and industrial production. We specifically consider an elastic linear object where one of its endpoints is fixed, and another point can be grasped by a robotic arm. To deal with the mentioned problem, we propose a model-free method to control the state of an arbitrary point that can be at any place along the object’s length. Our approach allows the robot to manipulate the object without knowing any model parameters or offline information of the object’s deformation. An adaptive control strategy is proposed for regulating the state of any point automatically deforming the object into the desired location. A control law is developed to regulate the object’s shape thanks to the adaptive estimation of the system parameters and its states. This method can track a desired manipulation trajectory to reach the target point, which leads to a smooth deformation without drastic changes. A Lyapunov-based argument is presented for the asymptotic convergence of the system that shows the process’s stability and convergence to desired state values. To validate the controller, numerical simulations involving two different deformation models are conducted, and performances of the proposed algorithm are investigated through full-scale experiments.

Reliable force-driven robot-interaction requires precise contact wrench measurements. In most robot systems these measurements are severely incorrect and in most manipulation tasks expensive additional force sensors are installed. We follow a learning approach to train the dependencies between joint torques and end-effector contact wrenches. We used a redundant serial light-weight manipulator (KUKA iiwa 7 R800) with integrated force estimation based on the joint torques measured in each of the robot’s seven axes. Firstly, a simulated dataset is created to let a feed-forward net learn the relationship between end-effector contact wrenches and joint torques for a static case. Secondly, an extensive real training dataset was acquired with 330,000 randomized robot positions and end-effector contact wrenches and used for retraining the simulated trained feed-forward net. We can show that the wrench prediction error could be reduced by around 57% for the forces compared to the manufacturer’s proprietary force estimation model. In addition, we show that the number of high outliers can be reduced substantially. Furthermore we prove that the approach could be also transferred to another robot (KUKA iiwa 14 R820) with reasonable prediction accuracy and without the need of acquiring new robot specific data.



Over the last decade or so, we’ve seen an enormous variety of jumping robots. With a few exceptions, these robots look to biology to inspire their design and functionality. This makes sense, because the natural world is full of jumping animals that are absolutely incredible, and matching their capabilities with robots seems like a reasonable thing to aspire to—with creatures such as ants, frogs, birds, and galagos, robots have tried (and occasionally succeeded in some specific ways) to mimic their motions.

The few exceptions to this bioinspired approach have included robots that leverage things like compressed gas and even explosives to jump in ways that animals cannot. The performance of these robots is very impressive, at least partially because their jumping techniques don’t get all wrapped up in biological models that tend to be influenced by non-jumping things, like versatility.

For a group of roboticists from UCSB and Disney Research, this led to a simple question: if you were to build a robot that focused exclusively on jumping as high as possible, how high could it jump? And in a paper published today in Nature, they answer that question with a robot that can jump 33 meters high, which reaches right about eyeball level on the Statue of Liberty.

These videos are unfortunately not all that great, but here’s a decent one of the jumping robot (which the researchers creatively refer to as “our jumper”) launching itself, landing, self-righting, and then launching again.

And here’s a slow-motion close up of the jump itself.

The jumper is 30 centimeters tall and weighs 30 grams, which is relatively heavy for a robot like this. It’s made almost entirely of carbon fiber bows that act as springs, along with rubber bands that store energy in tension. The center bit of the robot includes a motor, some batteries, and a latching mechanism attached to a string that connects the top of the robot to the bottom. To prepare for a jump, the robot starts spinning its motor, which over the course of two minutes winds up the string, squishing the robot down and gradually storing up a kind of ridiculous amount of energy. Once the string is almost completely wound up, one additional tug from the motor trips the latching mechanism which lets go of the string and releases all of the energy in approximately 9 milliseconds, over which time the robot accelerates from zero to 28 meters per second. All in, the robot has a specific energy of over 1,000 joules per kilogram, which is about an order of magnitude better than any other jumping robot of any kind, and easily trounces even the best biological jumpers by a factor of five.

The reason that this robot can jump as high as it does is because it relies on a clever bit of engineering that you won’t find anywhere (well, almost anywhere) in biology: a rotary motor. With a rotary motor and some gears attached to a spring, you can use a relatively low amount of power over a relatively long period of time to store lots and lots of energy as the motor spins. Animals don’t have access to rotary motors, so while they do have access to springs (tendons), the amount that those springs can be charged up for jumping is limited by how much you can do with the single power stroke that you get from a muscle. The upshot here is that the best biological jumpers, like the galago, simply have the biggest jumping muscles relative to their body mass. This is fine, but it’s a pretty significant limitation to how high animals can possibly jump.

While many other robots (stretching back at least a decade) have combined rotary motors and springs for jumping, the key insight that led to this Nature paper is the understanding that that the best way to engineer an optimal jumping robot is by completely inverting the biology: instead of getting bigger jumps through bigger motors, you instead minimize the motor while using as many tricks as possible to go all-in on the spring. The researchers were able to model the ratio of muscle to tendon for biological jumpers, and found that the best performance comes from a muscle that’s about 30 times the mass of the tendon. But for an engineered jumper, this paper shows that you actually want to invert that mass ratio, and this jumping robot has a spring that’s 1.2 times the mass of the motor. “We were too tied to the animal model,” co-author Morgan Pope from Disney Research told IEEE Spectrum. “So we’ve been jumping a few meters high when we should be jumping tens of meters high.”


A series of high speed images showing the robot releasing the tension in its springs and jumping

“Seeing our robot jump for the first time was magical,” first author Elliot Hawkes from UCSB told us. “We started with a design much more like a pogo stick before coming to a bow design, then to the hybrid spring design with the rubber bands and bows together. Countless hours went into troubleshooting all kinds of challenging mechanical problems, from gearbox teeth shearing off to hinges breaking to carbon-fiber springs exploding. Every new iteration was just as exciting—the most recent one that jumps over 30 meters just blows your mind when you see it take off in person. It’s so much energy in such a small device!”

Getting the robot to jump even higher (since Statue of Liberty eyeball-height obviously just isn’t good enough) will likely involve using a spring that’s even spring-ier to maximize the amount of energy that the robot can store without increasing its mass. “We have pushed the energy storage pretty far with our hybrid tension-compression spring,” Hawkes says. “But I believe there could be spring designs that could push this even further. We’re at around 2000 joules per kilogram right now.”

It’s temping to fixate on the bonkers jump height of this robot and wonder why we don’t toss all those other bio-inspired robots out the window, but it’s important to understand that this thing is very much a unitasker in a way that animals (and the robots built with animals in mind) are not. “We have made an incredibly specialized device that does one thing very well,” says Hawkes. It jumps very high once in a while. Biological jumpers do many other things way better, and are way more robust.”

With that in mind, it’s true that even the current version of this jumping robot can self-right, jump repetitively, and carry a small payload, like a camera. The researchers suggest that this combination of mobility and efficiency might make it ideal for exploring space, where jumping can get you a lot farther. On the moon, for example, this robot would be able to cover half a kilometer per jump, thanks to lower gravity and no atmospheric drag. “The application we are currently most excited about is space exploration,” Hawkes tells us. “The moon is a truly ideal location for jumping, which opens up new possibilities for exploration because it could overcome challenging terrain. For instance, the robot could hop onto the side of an inaccessible cliff or leap into the bottom of a crater, take samples, and return to a wheeled rover.” Hawkes says that he and his team are currently working with NASA to develop this system with the goal of launching to the moon within the next five years.

Engineered jumpers overcome biological limits via work multiplication, by Elliot W. Hawkes, Charles Xiao, Richard-Alexandre Peloquin, Christopher Keeley, Matthew R. Begley, Morgan T. Pope, and Günter Niemeyer from UCSB, Disney Research, and Caltech, appears this week in Nature.


Over the last decade or so, we’ve seen an enormous variety of jumping robots. With a few exceptions, these robots look to biology to inspire their design and functionality. This makes sense, because the natural world is full of jumping animals that are absolutely incredible, and matching their capabilities with robots seems like a reasonable thing to aspire to—with creatures such as ants, frogs, birds, and galagos, robots have tried (and occasionally succeeded in some specific ways) to mimic their motions.

The few exceptions to this bioinspired approach have included robots that leverage things like compressed gas and even explosives to jump in ways that animals cannot. The performance of these robots is very impressive, at least partially because their jumping techniques don’t get all wrapped up in biological models that tend to be influenced by non-jumping things, like versatility.

For a group of roboticists from UCSB and Disney Research, this led to a simple question: if you were to build a robot that focused exclusively on jumping as high as possible, how high could it jump? And in a paper published today in Nature, they answer that question with a robot that can jump 33 meters high, which reaches right about eyeball level on the Statue of Liberty.

These videos are unfortunately not all that great, but here’s a decent one of the jumping robot (which the researchers creatively refer to as “our jumper”) launching itself, landing, self-righting, and then launching again.

And here’s a slow-motion close up of the jump itself.

The jumper is 30 centimeters tall and weighs 30 grams, which is relatively heavy for a robot like this. It’s made almost entirely of carbon fiber bows that act as springs, along with rubber bands that store energy in tension. The center bit of the robot includes a motor, some batteries, and a latching mechanism attached to a string that connects the top of the robot to the bottom. To prepare for a jump, the robot starts spinning its motor, which over the course of two minutes winds up the string, squishing the robot down and gradually storing up a kind of ridiculous amount of energy. Once the string is almost completely wound up, one additional tug from the motor trips the latching mechanism which lets go of the string and releases all of the energy in approximately 9 milliseconds, over which time the robot accelerates from zero to 28 meters per second. All in, the robot has a specific energy of over 1,000 joules per kilogram, which is about an order of magnitude better than any other jumping robot of any kind, and easily trounces even the best biological jumpers by a factor of five.

The reason that this robot can jump as high as it does is because it relies on a clever bit of engineering that you won’t find anywhere (well, almost anywhere) in biology: a rotary motor. With a rotary motor and some gears attached to a spring, you can use a relatively low amount of power over a relatively long period of time to store lots and lots of energy as the motor spins. Animals don’t have access to rotary motors, so while they do have access to springs (tendons), the amount that those springs can be charged up for jumping is limited by how much you can do with the single power stroke that you get from a muscle. The upshot here is that the best biological jumpers, like the galago, simply have the biggest jumping muscles relative to their body mass. This is fine, but it’s a pretty significant limitation to how high animals can possibly jump.

While many other robots (stretching back at least a decade) have combined rotary motors and springs for jumping, the key insight that led to this Nature paper is the understanding that that the best way to engineer an optimal jumping robot is by completely inverting the biology: instead of getting bigger jumps through bigger motors, you instead minimize the motor while using as many tricks as possible to go all-in on the spring. The researchers were able to model the ratio of muscle to tendon for biological jumpers, and found that the best performance comes from a muscle that’s about 30 times the mass of the tendon. But for an engineered jumper, this paper shows that you actually want to invert that mass ratio, and this jumping robot has a spring that’s 1.2 times the mass of the motor. “We were too tied to the animal model,” co-author Morgan Pope from Disney Research told IEEE Spectrum. “So we’ve been jumping a few meters high when we should be jumping tens of meters high.”


A series of high speed images showing the robot releasing the tension in its springs and jumping

“Seeing our robot jump for the first time was magical,” first author Elliot Hawkes from UCSB told us. “We started with a design much more like a pogo stick before coming to a bow design, then to the hybrid spring design with the rubber bands and bows together. Countless hours went into troubleshooting all kinds of challenging mechanical problems, from gearbox teeth shearing off to hinges breaking to carbon-fiber springs exploding. Every new iteration was just as exciting—the most recent one that jumps over 30 meters just blows your mind when you see it take off in person. It’s so much energy in such a small device!”

Getting the robot to jump even higher (since Statue of Liberty eyeball-height obviously just isn’t good enough) will likely involve using a spring that’s even spring-ier to maximize the amount of energy that the robot can store without increasing its mass. “We have pushed the energy storage pretty far with our hybrid tension-compression spring,” Hawkes says. “But I believe there could be spring designs that could push this even further. We’re at around 2000 joules per kilogram right now.”

It’s temping to fixate on the bonkers jump height of this robot and wonder why we don’t toss all those other bio-inspired robots out the window, but it’s important to understand that this thing is very much a unitasker in a way that animals (and the robots built with animals in mind) are not. “We have made an incredibly specialized device that does one thing very well,” says Hawkes. It jumps very high once in a while. Biological jumpers do many other things way better, and are way more robust.”

With that in mind, it’s true that even the current version of this jumping robot can self-right, jump repetitively, and carry a small payload, like a camera. The researchers suggest that this combination of mobility and efficiency might make it ideal for exploring space, where jumping can get you a lot farther. On the moon, for example, this robot would be able to cover half a kilometer per jump, thanks to lower gravity and no atmospheric drag. “The application we are currently most excited about is space exploration,” Hawkes tells us. “The moon is a truly ideal location for jumping, which opens up new possibilities for exploration because it could overcome challenging terrain. For instance, the robot could hop onto the side of an inaccessible cliff or leap into the bottom of a crater, take samples, and return to a wheeled rover.” Hawkes says that he and his team are currently working with NASA to develop this system with the goal of launching to the moon within the next five years.

Engineered jumpers overcome biological limits via work multiplication, by Elliot W. Hawkes, Charles Xiao, Richard-Alexandre Peloquin, Christopher Keeley, Matthew R. Begley, Morgan T. Pope, and Günter Niemeyer from UCSB, Disney Research, and Caltech, appears this week in Nature.

The development of new sensory and robotic technologies in recent years and the increase in the consumption of organic vegetables have allowed the generation of specific applications around precision agriculture seeking to satisfy market demand. This article analyzes the use and advantages of specific optical sensory systems for data acquisition and processing in precision agriculture for Robotic Fertilization process. The SUREVEG project evaluates the benefits of growing vegetables in rows, using different technological tools like sensors, embedded systems, and robots, for this purpose. A robotic platform has been developed consisting of Laser Sick AG LMS100 × 3, Multispectral, RGB sensors, and a robotic arm equipped with a fertilization system. Tests have been developed with the robotic platform in cabbage and red cabbage crops, information captured with the different sensors, allowed to reconstruct rows crops and extract information for fertilization with the robotic arm. The main advantages of each sensory have been analyzed with an quantitative comparison, based on information provided by each one; such as Normalized Difference Vegetation Index index, RGB Histograms, Point Cloud Clusters). Robot Operating System processes this information to generate trajectory planning with the robotic arm and apply the individual treatment in plants. Main results show that the vegetable characterization has been carried out with an efficiency of 93.1% using Point Cloud processing, while the vegetable detection has obtained an error of 4.6% through RGB images.

We propose a segment design that combines two distinct characteristics of tendon-driven continuum robots, i.e. variable length and non-straight tendon routing, into a single segment by enabling rotation of its backbone. As a result, this segment can vary its helical tendon routing and has four degrees-of-freedom, while maintaining a small-scale design with an overall outer diameter of 7 mm thanks to an extrinsic actuation principle. In simulation and on prototypes, we observe improved motion capabilities, as evidenced by position redundancy and follow-the-leader deployment along spatially tortuous paths. To demonstrate the latter on a physical prototype, a simple, yet effective area-based error measure for follow-the-leader deployment is proposed to evaluate the performance. Furthermore, we derive a static model which is used to underpin the observed motion capabilities. In summary, our segment design extends previous designs with minimal hardware overhead, while either archiving similar accuracy in position errors and planar follow-the-leader deployment, or exhibiting superior motion capabilities due to position redundancy and spatial follow-the-leader deployment.

Evaluating the dexterity of human and robotic hands through appropriate benchmarks, scores, and metrics is of paramount importance for determining how skillful humans are and for designing and developing new bioinspired or even biomimetic end-effectors (e.g., robotic grippers and hands). Dexterity tests have been used in industrial and medical settings to assess how dexterous the hands of workers and surgeons are as well as in robotic rehabilitation settings to determine the improvement or deterioration of the hand function after a stroke or a surgery. In robotics, having a comprehensive dexterity test can allow us to evaluate and compare grippers and hands irrespectively of their design characteristics. However, there is a lack of well defined metrics, benchmarks, and tests that quantify robot dexterity. Previous work has focused on a number of widely accepted functional tests that are used for the evaluation of manual dexterity and human hand function improvement post injury. Each of these tests focuses on a different set of specific tasks and objects. Deriving from these tests, this work proposes a new modular, affordable, accessible, open-source dexterity test for both humans and robots. This test evaluates the grasping and manipulation capabilities by combining the features and best practices of the aforementioned tests, as well as new task categories specifically designed to evaluate dexterous manipulation capabilities. The dexterity test and the accompanying benchmarks allow us to determine the overall hand function recovery and dexterity of robotic end-effectors with ease. More precisely, a dexterity score that ranges from 0 (simplistic, non-dexterous system) to 1 (human-like system) is calculated using the weighted sum of the accuracy and task execution speed subscores. It should also be noted that the dexterity of a robotic system can be evaluated assessing the efficiency of either the robotic hardware, or the robotic perception system, or both. The test and the benchmarks proposed in the study have been validated using extensive human and robot trials. The human trials have been used to determine the baseline scores for the evaluation system. The results show that the time required to complete the tasks reduces significantly with trials indicating a clear learning curve in mastering the dexterous manipulation capabilities associated with the imposed tasks. Finally, the time required to complete the tasks with restricted tactile feedback is significantly higher indicating its importance.

Proportional control using surface electromyography (EMG) enables more intuitive control of a transfemoral prosthesis. However, EMG is a noisy signal which can vary over time, giving rise to the question what approach for knee torque estimation is most suitable for multi-day control. In this study we compared three different modelling frameworks to estimate knee torque in non-weight-bearing situations. The first model contained a convolutional neural network (CNN) which mapped EMG to knee torque directly. The second used a neuromusculoskeletal model (NMS) which used EMG, muscle tendon unit lengths and moment arms to compute knee torque. The third model (Hybrid) used a CNN to map EMG to specific muscle activation, which was used together with NMS components to compute knee torque. Multi-day measurements were conducted on ten able-bodied participants who performed non-weight bearing activities. CNN had the best performance in general and on each day (Normalized Root Mean Squared Error (NRMSE) 9.2 ± 4.4%). The Hybrid model (NRMSE 12.4 ± 3.4%) was able to outperform NMS (NRMSE 14.3 ± 4.2%). The NMS model showed no significant difference between measurement days. The CNN model and Hybrid models had significant performance differences between the first day and all other days. CNNs are suited for multi-day torque estimation in terms of error rate, outperforming the other two model types. NMS was the only model type which was robust over all days. This study investigated the behavior of three model types over multiple days, giving insight in the most suited modelling approach for multi-day torque estimation to be used in prosthetic control.

This paper proposes the use of the standing waves created by the interference between transmitted and reflected acoustic signals to recognize the size and the shape of a target object. This study shows that the profile of the distance spectrum generated by the interference encodes not only the distance to the target, but also the distance to the edges of the target surface. To recognize the extent of the surface, a high-resolution distance spectrum is proposed, and a method to estimate the points on the edges by incorporating observations from multiple measurement is introduced. Numerical simulations validated the approach and showed that the method worked even in the presence of noise. Experimental results are also shown to verify that the method works in a real environment.

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