Microfliers, or miniature wireless robots deployed in numbers, are sometimes used today for large-scale surveillance and monitoring purposes, such as in environmental or biological studies. Because of the fliers’ ability to disperse in air, they can spread out to cover large areas after being dropped from a single location, including in places where access is otherwise difficult. Plus, they are smaller, lighter, and cheaper to deploy than multiple drones.

One of the challenges in creating more efficient microfliers has been in reducing power consumption. One way to do so, as researchers from the University of Washington (UW) and Université Grenoble Alpes have demonstrated, is to get rid of the battery. With inspiration from the Japanese art of paper folding, origami, they designed programmable microfliers that can disperse in the wind and change shape using electronic actuation. This is achieved by a solar-powered actuator that can produce up to 200 millinewtons of force in 25 milliseconds.

“Think of these little fliers as a sensor platform to measure environmental conditions, like, temperature, light, and other things.”
—Vikram Iyer, University of Washington

“The cool thing about these origami designs is, we’ve created a way for them to change shape in midair, completely battery free,” says Vikram Iyer, computer scientist and engineer at UW, one of the authors. “It’s a pretty small change in shape, but it creates a very dramatic change in falling behavior…that allows us to get some control over how these things are flying.”

Tumbling and stable states: A) The origami microflier here is in its tumbling state and B) postlanding configuration. As it descends, the flier tumbles, with a typical tumbling pattern pictured in C. D) The origami microflier is here in its stable descent state. The fliers’ range of landing locations, E, reveals its dispersal patterns after being released from its parent drone. Vicente Arroyos, Kyle Johnson, and Vikram Iyer/University of Washington

This research builds on the researchers’ earlier work published in 2022, demonstrating sensors that can disperse in air like dandelion seeds. For the current study, “the goal was to deploy hundreds of these sensors and control where they land, to achieve precise deployments,” says coauthor Shyamnath Gollakota, who leads the Mobile Intelligence Lab at WU. The microfliers, each weighing less than 500 milligrams, can travel almost 100 meters in a light breeze, and wirelessly transmit data about air pressure and temperature via Bluetooth up to a distance of 60 meters. The group’s findings were published in Science Robotics earlier this month.

Discovering the difference in the falling behavior of the two origami states was serendipity, Gollakota says: “When it is flat, it’s almost like a leaf, tumbling [in the] the wind,” he says. “A very slight change from flat to a little bit of a curvature [makes] it fall like a parachute in a very controlled motion.” In their tumbling state, in lateral wind gusts, the microfliers achieve up to three times the dispersal distance as in their stable state, he adds.

This close-up of the microflier reveals the electronics and circuitry on its top side.Vicente Arroyos, Kyle Johnson, and Vikram Iyer/University of Washington

There have been other origami-based systems in which motors, electrostatic actuators, shape-memory alloys, and electrothermal polymers, for example, have been used, but these did not address the challenges facing the researchers, Gollakota says. One was to find the sweet spot between an actuation mechanism strong enough to not change shape without being triggered, yet lightweight enough to keep power consumption low. Next, it had to produce a rapid transition response while falling to the ground. Finally, it needed to have a lightweight energy storage solution onboard to trigger the transition.

The mechanism, which Gollakota describes as “pretty commonsensical” still took them a year to come up with. There’s a stem in the middle of the origami, comprising a solenoid coil (a coil that acts as a magnet when a current passes through it), and two small magnets. Four hinged carbon-fiber rods attach the stem to the edges of the structure. When a pulse of current is applied to the solenoid coil, it pushes the magnets toward each other, making the structure snap into its alternative shape.

All it requires is a tiny bit of power, just enough to put the magnets within the right distance from each other for the magnetic forces to work, Gollakota says. There is an array of thin, lightweight solar cells to harvest energy, which is stored in a little capacitor. The circuit is fabricated directly on the foldable origami structure, and also includes a microcontroller, timer, Bluetooth receiver, and pressure and temperature sensors.

“We can program these things to trigger the shape change based on any of these things—after a fixed time, when we send it a radio signal, or, at an altitude [or temperature] that this device detects,” Iyer adds. The origami structure is bistable, meaning it does not need any energy to maintain shape once it has transitioned.

The researchers say their design can be extended to incorporate sensors for a variety of environmental monitoring applications. “Think of these little fliers as a sensor platform to measure environmental conditions, like temperature, light, and other things, [and] how they vary throughout the atmosphere,” Iyer says. Or they can deploy sensors on the ground for things like digital agriculture, climate change–related studies, and tracking forest fires.

In their current prototype, the microfliers only shape-change in one direction, but the researchers want to make them transition in both directions, to be able to toggle the two states, and control the trajectory even better. They also imagine a swarm of microfliers communicating with one another, controlling their behavior, and self-organizing how they are falling and dispersing.

Microfliers, or miniature wireless robots deployed in numbers, are sometimes used today for large-scale surveillance and monitoring purposes, such as in environmental or biological studies. Because of the fliers’ ability to disperse in air, they can spread out to cover large areas after being dropped from a single location, including in places where access is otherwise difficult. Plus, they are smaller, lighter, and cheaper to deploy than multiple drones.

One of the challenges in creating more efficient microfliers has been in reducing power consumption. One way to do so, as researchers from the University of Washington (UW) and Université Grenoble Alpes have demonstrated, is to get rid of the battery. With inspiration from the Japanese art of paper folding, origami, they designed programmable microfliers that can disperse in the wind and change shape using electronic actuation. This is achieved by a solar-powered actuator that can produce up to 200 millinewtons of force in 25 milliseconds.

“Think of these little fliers as a sensor platform to measure environmental conditions, like, temperature, light, and other things.”
—Vikram Iyer, University of Washington

“The cool thing about these origami designs is, we’ve created a way for them to change shape in midair, completely battery free,” says Vikram Iyer, computer scientist and engineer at UW, one of the authors. “It’s a pretty small change in shape, but it creates a very dramatic change in falling behavior…that allows us to get some control over how these things are flying.”

Tumbling and stable states: A) The origami microflier here is in its tumbling state and B) postlanding configuration. As it descends, the flier tumbles, with a typical tumbling pattern pictured in C. D) The origami microflier is here in its stable descent state. The fliers’ range of landing locations, E, reveals its dispersal patterns after being released from its parent drone. Vicente Arroyos, Kyle Johnson, and Vikram Iyer/University of Washington

This research builds on the researchers’ earlier work published in 2022, demonstrating sensors that can disperse in air like dandelion seeds. For the current study, “the goal was to deploy hundreds of these sensors and control where they land, to achieve precise deployments,” says coauthor Shyamnath Gollakota, who leads the Mobile Intelligence Lab at WU. The microfliers, each weighing less than 500 milligrams, can travel almost 100 meters in a light breeze, and wirelessly transmit data about air pressure and temperature via Bluetooth up to a distance of 60 meters. The group’s findings were published in Science Robotics earlier this month.

Discovering the difference in the falling behavior of the two origami states was serendipity, Gollakota says: “When it is flat, it’s almost like a leaf, tumbling [in the] the wind,” he says. “A very slight change from flat to a little bit of a curvature [makes] it fall like a parachute in a very controlled motion.” In their tumbling state, in lateral wind gusts, the microfliers achieve up to three times the dispersal distance as in their stable state, he adds.

This close-up of the microflier reveals the electronics and circuitry on its top side.Vicente Arroyos, Kyle Johnson, and Vikram Iyer/University of Washington

There have been other origami-based systems in which motors, electrostatic actuators, shape-memory alloys, and electrothermal polymers, for example, have been used, but these did not address the challenges facing the researchers, Gollakota says. One was to find the sweet spot between an actuation mechanism strong enough to not change shape without being triggered, yet lightweight enough to keep power consumption low. Next, it had to produce a rapid transition response while falling to the ground. Finally, it needed to have a lightweight energy storage solution onboard to trigger the transition.

The mechanism, which Gollakota describes as “pretty commonsensical” still took them a year to come up with. There’s a stem in the middle of the origami, comprising a solenoid coil (a coil that acts as a magnet when a current passes through it), and two small magnets. Four hinged carbon-fiber rods attach the stem to the edges of the structure. When a pulse of current is applied to the solenoid coil, it pushes the magnets toward each other, making the structure snap into its alternative shape.

All it requires is a tiny bit of power, just enough to put the magnets within the right distance from each other for the magnetic forces to work, Gollakota says. There is an array of thin, lightweight solar cells to harvest energy, which is stored in a little capacitor. The circuit is fabricated directly on the foldable origami structure, and also includes a microcontroller, timer, Bluetooth receiver, and pressure and temperature sensors.

“We can program these things to trigger the shape change based on any of these things—after a fixed time, when we send it a radio signal, or, at an altitude [or temperature] that this device detects,” Iyer adds. The origami structure is bistable, meaning it does not need any energy to maintain shape once it has transitioned.

The researchers say their design can be extended to incorporate sensors for a variety of environmental monitoring applications. “Think of these little fliers as a sensor platform to measure environmental conditions, like temperature, light, and other things, [and] how they vary throughout the atmosphere,” Iyer says. Or they can deploy sensors on the ground for things like digital agriculture, climate change–related studies, and tracking forest fires.

In their current prototype, the microfliers only shape-change in one direction, but the researchers want to make them transition in both directions, to be able to toggle the two states, and control the trajectory even better. They also imagine a swarm of microfliers communicating with one another, controlling their behavior, and self-organizing how they are falling and dispersing.

Introduction: Geometric pattern formation is crucial in many tasks involving large-scale multi-agent systems. Examples include mobile agents performing surveillance, swarms of drones or robots, and smart transportation systems. Currently, most control strategies proposed to achieve pattern formation in network systems either show good performance but require expensive sensors and communication devices, or have lesser sensor requirements but behave more poorly.

Methods and result: In this paper, we provide a distributed displacement-based control law that allows large groups of agents to achieve triangular and square lattices, with low sensor requirements and without needing communication between the agents. Also, a simple, yet powerful, adaptation law is proposed to automatically tune the control gains in order to reduce the design effort, while improving robustness and flexibility.

Results: We show the validity and robustness of our approach via numerical simulations and experiments, comparing it, where possible, with other approaches from the existing literature.

The concept of sustainability and sustainable development has been well discussed and was subject to many conferences of the EU and UN resulting in agendas, goals, and resolutions. Yet, literature shows that the three dimensions of sustainability (ecological, social, and economic) are unevenly accounted for in the design of mechatronic products. The stated reasons range from a lack or inapplicability of tools for integration into the design process, models for simulation, and impact analyses to necessary changes in policy and social behavior. The influence designers have on the sustainability of a product lies mostly in the early design phases of the development process, such as requirements engineering and concept evaluation. Currently, these concepts emerge mostly from performance-based requirements rather than sustainability impact-based requirements, which are also true for service robots in urban environments. So far, the main focus of research in this innovative and growing product branch lies in performance in perception, navigation, and interaction. This paper sets its focus on integrating all three dimensions of sustainability into the design process. Therefore, we describe the development of an urban service robot supporting municipal waste management in the city of Berlin. It is the set goal for the robot to increase the service and support the employees while reducing emissions. For that, we make use of a product development process (PDP) and its adaptable nature to build a specific development process suited to include the three dimensions of sustainability during the requirements engineering and evaluation activities. Herein, we show how established design methods like the life cycle assessment or life cycle costing can be applied to the development of urban service robots and which aspects are underrepresented. Especially, the social dimension required us to look beyond standardized methods in the field of mechanical engineering. Based on our findings, we introduce a new activity to the development process that we call preliminary social assessment in order to incorporate social aspects in the early design phase.

6D pose recognition has been a crucial factor in the success of robotic grasping, and recent deep learning based approaches have achieved remarkable results on benchmarks. However, their generalization capabilities in real-world applications remain unclear. To overcome this gap, we introduce 6IMPOSE, a novel framework for sim-to-real data generation and 6D pose estimation. 6IMPOSE consists of four modules: First, a data generation pipeline that employs the 3D software suite Blender to create synthetic RGBD image datasets with 6D pose annotations. Second, an annotated RGBD dataset of five household objects was generated using the proposed pipeline. Third, a real-time two-stage 6D pose estimation approach that integrates the object detector YOLO-V4 and a streamlined, real-time version of the 6D pose estimation algorithm PVN3D optimized for time-sensitive robotics applications. Fourth, a codebase designed to facilitate the integration of the vision system into a robotic grasping experiment. Our approach demonstrates the efficient generation of large amounts of photo-realistic RGBD images and the successful transfer of the trained inference model to robotic grasping experiments, achieving an overall success rate of 87% in grasping five different household objects from cluttered backgrounds under varying lighting conditions. This is made possible by fine-tuning data generation and domain randomization techniques and optimizing the inference pipeline, overcoming the generalization and performance shortcomings of the original PVN3D algorithm. Finally, we make the code, synthetic dataset, and all the pre-trained models available on GitHub.

There seems to be two general approaches to cooking automation. There’s the “let’s make a robot that can operate in a human kitchen because everyone has a human kitchen,” which seems like a good idea, except that you then have to build your robot to function in human environments which is super hard. On the other end of the spectrum, there’s the “let’s make a dedicated automated system because automation is easier than robotics,” which seems like a good idea, except that you then have to be willing to accept compromises in recipes and texture and taste because preparing food in an automated way just does not yield the same result, as anyone who has ever attempted to Cuisinart their way out of developing some knife skills can tell you.

The Robotics and Mechanisms Lab (RoMeLa) at UCLA, run by Dennis Hong, has been working on a compromise approach that leverages both robot-friendly automation and the kind of human skills that make things taste right. Called Project YORI, which somehow stands for “Yummy Operations Robot Initiative” while also meaning “cooking” in Korean, the system combines a robot-optimized environment with a pair of arms that can operate kitchen tools sort of like a human.

“Instead of trying to mimic how humans cook,” the researchers say, “we approached the problem by thinking how cooking would be accomplished if a robot cooks. Thus the YORI system does not use the typical cooking methods, tools or utensils which are developed for humans.” In addition to a variety of automated cooking systems, the tools that YORI does use are modified to work with a tool changing system, which mostly eliminates the problem of grasping something like a knife well enough that you can precisely and repeatedly exert a substantial amount of force through it, and also helps keep things structured and accessible.

In terms of cooking methods, the system takes advantage of technology when and where it works better than conventional human cooking techniques. For example, in order to tell whether ingredients are fresh or to determine when food is cooked ideally, YORI “utilizes unique chemical sensors,” which I guess are the robot equivalent of a nose and taste buds and arguably would do a more empirical job than some useless recipe metric like “season to taste.”

The advantage of a system like this is versatility. In theory, it’s not as constrained by recipes that you can cram into a system built around automation because of those added robotic capabilities, while also being somewhat practical—or at least, more practical than a robot designed to interact with a lightly modified human kitchen. And it’s actually designed to be practical(ish), in the sense that it’s being developed under a partnership with Woowa Brothers, the company that runs the leading food delivery service in South Korea. It’s obviously still a work in progress—you can see a human hand sneaking in there from time to time. But the approach seems interesting, and I hope that RoMeLa keeps making progress on it, because I’m hungry.

There seems to be two general approaches to cooking automation. There’s the “let’s make a robot that can operate in a human kitchen because everyone has a human kitchen,” which seems like a good idea, except that you then have to build your robot to function in human environments which is super hard. On the other end of the spectrum, there’s the “let’s make a dedicated automated system because automation is easier than robotics,” which seems like a good idea, except that you then have to be willing to accept compromises in recipes and texture and taste because preparing food in an automated way just does not yield the same result, as anyone who has ever attempted to Cuisinart their way out of developing some knife skills can tell you.

The Robotics and Mechanisms Lab (RoMeLa) at UCLA, run by Dennis Hong, has been working on a compromise approach that leverages both robot-friendly automation and the kind of human skills that make things taste right. Called Project YORI, which somehow stands for “Yummy Operations Robot Initiative” while also meaning “cooking” in Korean, the system combines a robot-optimized environment with a pair of arms that can operate kitchen tools sort of like a human.

“Instead of trying to mimic how humans cook,” the researchers say, “we approached the problem by thinking how cooking would be accomplished if a robot cooks. Thus the YORI system does not use the typical cooking methods, tools or utensils which are developed for humans.” In addition to a variety of automated cooking systems, the tools that YORI does use are modified to work with a tool changing system, which mostly eliminates the problem of grasping something like a knife well enough that you can precisely and repeatedly exert a substantial amount of force through it, and also helps keep things structured and accessible.

In terms of cooking methods, the system takes advantage of technology when and where it works better than conventional human cooking techniques. For example, in order to tell whether ingredients are fresh or to determine when food is cooked ideally, YORI “utilizes unique chemical sensors,” which I guess are the robot equivalent of a nose and taste buds and arguably would do a more empirical job than some useless recipe metric like “season to taste.”

The advantage of a system like this is versatility. In theory, it’s not as constrained by recipes that you can cram into a system built around automation because of those added robotic capabilities, while also being somewhat practical—or at least, more practical than a robot designed to interact with a lightly modified human kitchen. And it’s actually designed to be practical(ish), in the sense that it’s being developed under a partnership with Woowa Brothers, the company that runs the leading food delivery service in South Korea. It’s obviously still a work in progress—you can see a human hand sneaking in there from time to time. But the approach seems interesting, and I hope that RoMeLa keeps making progress on it, because I’m hungry.

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

Enjoy today’s videos!

Wow! Okay, all robotics videos definitely need confetti cannons.

[ DFKI ]

What an incredibly relaxing robot video this is.

Except for the tree bit, I mean.

[ Paper ] via [ ASL ]

Skydio has a fancy new drone, but not for you!

[ Skydio X10 ]

[ Paper ] via [ VISTEC ]

Thanks, Poramate!

I’m not totally sure how you’d control all of these extra arms in a productive way, but I’m sure they’ll figure it out!

[ KIMLAB ]

[ Deep Robotics ]

They’re calling this the “T-REX” but without a pair of tiny arms. Missed opportunity there.

[ AgileX ]

[ NASA ]

I don’t know why you need to drive out into the woods to drop-test your sensor rack. Though maybe the stunning Canadian backwoods scenery is reason enough.

[ NORLab ]

If they ever make Reachy’s face symmetrical, I will refuse to include it in any more Video Fridays. O_o

[ Pollen Robotics ]

[ UZH RPG ]

[ MPlusPlus ] via [ Kazumichi Moriyama ]

[ Extend Robotics ]

Here’s what’s been going on at the GRASP Lab at UPenn.

[ GRASP Lab ]

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

Enjoy today’s videos!

Wow! Okay, all robotics videos definitely need confetti cannons.

[ DFKI ]

What an incredibly relaxing robot video this is.

Except for the tree bit, I mean.

[ Paper ] via [ ASL ]

Skydio has a fancy new drone, but not for you!

[ Skydio X10 ]

[ Paper ] via [ VISTEC ]

Thanks, Poramate!

I’m not totally sure how you’d control all of these extra arms in a productive way, but I’m sure they’ll figure it out!

[ KIMLAB ]

[ Deep Robotics ]

They’re calling this the “T-REX” but without a pair of tiny arms. Missed opportunity there.

[ AgileX ]

[ NASA ]

I don’t know why you need to drive out into the woods to drop-test your sensor rack. Though maybe the stunning Canadian backwoods scenery is reason enough.

[ NORLab ]

If they ever make Reachy’s face symmetrical, I will refuse to include it in any more Video Fridays. O_o

[ Pollen Robotics ]

[ UZH RPG ]

[ MPlusPlus ] via [ Kazumichi Moriyama ]

[ Extend Robotics ]

Here’s what’s been going on at the GRASP Lab at UPenn.

[ GRASP Lab ]

This paper presents an in-pipe robot with three underactuated parallelogram crawler modules, which can automatically shift its body shape when encountering obstacles. The shape-shifting movement is achieved by only a single actuator through a simple differential mechanism by only combining a pair of spur gears. It can lead to downsizing, cost reduction, and simplification of control for adaptation to obstacles. The parallelogram shape does not change the total belt circumference length, thus, a new mechanism to maintain the belt tension is not necessary. Moreover, the proposed crawler can form the anterior-posterior symmetric parallelogram relative to the moving direction, which generates high adaptability in both forward and backward directions. However, whether the locomotion or shape-shifting is driven depends on the gear ratio of the differential mechanism because their movements are only switched mechanically. Therefore, to clarify the requirements of the gear ratio for the passive adaptation, two outputs of each crawler mechanism (torques of the flippers and front pulley) are quasi-statically analyzed, and how the environmental and design parameters influence the robot performance are verified by real experiments. From the experiments, although the robot could not adapt to the stepped pipe in vertical section, it successfully shifted its crawler’s shape to parallelogram in horizontal section only with our simulated output ratio.

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

Enjoy today’s videos!

And you can make your own! The origami part, anyway:

[ Science Robotics ] via [ UW ]

Thanks, Sarah!

Also, please see our feature story on a related topic.

[ Science Robotics ]

Ascento has a fresh $4.3m in funding to develop its cute two-wheeled robot for less-cute security applications.

[ Ascento ]

Thanks, Miguel!

[ iRobot ]

[ Paper ] via [ KIMLAB ]

[ Apptronik ]

This robot plays my kind of Jenga.

This teleoperated robot was built by Lingkang Zhang, who tells us that it was inspired by Sanctuary AI’s robot.

[ HRC Model 4 ]

Thanks, Lingkang!

[ Paper ] via [ BruBotics ]

Thanks Bram!

[ Misty ]

Zoox is so classy that if you get in during the day and get out at night, it’ll apparently give you a free jean jacket.

[ Zoox ]

[ DeepRobotics ]

[ Cybathlon ]

[ NASA ]

[ Clearpath ]

[ Columbia ]

[ CRASAR ]

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

Enjoy today’s videos!

And you can make your own! The origami part, anyway:

[ Science Robotics ] via [ UW ]

Thanks, Sarah!

Also, please see our feature story on a related topic.

[ Science Robotics ]

Ascento has a fresh $4.3m in funding to develop its cute two-wheeled robot for less-cute security applications.

[ Ascento ]

Thanks, Miguel!

[ iRobot ]

[ Paper ] via [ KIMLAB ]

[ Apptronik ]

This robot plays my kind of Jenga.

This teleoperated robot was built by Lingkang Zhang, who tells us that it was inspired by Sanctuary AI’s robot.

[ HRC Model 4 ]

Thanks, Lingkang!

[ Paper ] via [ BruBotics ]

Thanks Bram!

[ Misty ]

Zoox is so classy that if you get in during the day and get out at night, it’ll apparently give you a free jean jacket.

[ Zoox ]

[ DeepRobotics ]

[ Cybathlon ]

[ NASA ]

[ Clearpath ]

[ Columbia ]

[ CRASAR ]

Soft robot’s natural dynamics calls for the development of tailored modeling techniques for control. However, the high-dimensional configuration space of the geometrically exact modeling approaches for soft robots, i.e., Cosserat rod and Finite Element Methods (FEM), has been identified as a key obstacle in controller design. To address this challenge, Reduced Order Modeling (ROM), i.e., the approximation of the full-order models, and Model Order Reduction (MOR), i.e., reducing the state space dimension of a high fidelity FEM-based model, are enjoying extensive research. Although both techniques serve a similar purpose and their terms have been used interchangeably in the literature, they are different in their assumptions and implementation. This review paper provides the first in-depth survey of ROM and MOR techniques in the continuum and soft robotics landscape to aid Soft Robotics researchers in selecting computationally efficient models for their specific tasks.

It’s hard to beat the energy density of chemical fuels. Batteries are quiet and clean and easy to integrate with electrically powered robots, but they’re 20 to 50 times less energy dense than a chemical fuel like methanol or butane. This is fine for most robots that can afford to just carry around a whole bunch of batteries, but as you start looking at robots that are insect-size or smaller, batteries simply don’t scale down very well. And it’s not just the batteries—electric actuators don’t scale down well either, especially if you’re looking for something that can generate a lot of power.

In a paper published 14 September in the journal Science, researchers from Cornell have tackled the small-scale actuation problem with what is essentially a very tiny, very soft internal-combustion engine. Methane vapor and oxygen are injected into a soft combustion chamber, where an itty-bitty li’l spark ignites the mixture. In half a millisecond, the top of the chamber balloons upward like a piston, generating forces of 9.5 newtons through a cycle that can repeat 100 times every second. Put two of these actuators together (driving two legs a piece) and you’ve got an exceptionally powerful soft quadruped robot.

Each of the two actuators powering this robot weighs just 325 milligrams and is about a quarter of the size of a U.S. penny. Part of the reason that they can be so small is that most of the associated components are off-board, including the fuel itself, the system that mixes and delivers the fuel, and the electrical source for the spark generator. But even without all of that stuff, the actuator has a bunch going on that enables it to operate continuously at high cycle frequencies without melting.

A view of the actuator and its component materials along with a diagram of the combustion actuation cycle.Science Robotics

The biggest issue may be that this actuator has to handle actual explosions, meaning that careful design is required to make sure that it doesn’t torch itself every time it goes off. The small combustion volume helps with this, as does the flame-resistant elastomer material and the integrated flame arrestor. Despite the violence inherent to how this actuator works, it’s actually very durable, and the researchers estimate that it can operate continuously for more than 750,000 cycles (8.5 hours at 50 hertz) without any drop in performance.

“What is interesting is just how powerful small-scale combustion is,” says Robert F. Shepherd, who runs the Organic Robotics Lab at Cornell. We covered some of Shepherd’s work on combustion-powered robots nearly a decade ago, with this weird pink jumping thing at IROS 2014. But going small has both challenges and benefits, Shepherd tells us. “We operate in the lower limit of what volumes of gases are combustible. It’s an interesting place for science, and the engineering outcomes are also useful.”

The first of those engineering outcomes is a little insect-scale quadrupedal robot that utilizes two of these soft combustion actuators to power a pair of legs each. The robot is 29 millimeters long and weighs just 1.6 grams, but it can jump a staggering 59 centimeters straight up and walk while carrying 22 times its own weight. For an insect-scale robot, Shepherd says, this is “near insect level performance, jumping extremely high, very quickly, and carrying large loads.”

Cornell University

It’s a little bit hard to see how the quadruped actually walks, since the actuators move so fast. Each actuator controls one side of the robot, with one combustion chamber connected to chambers at each foot with elastomer membranes. An advantage of this actuation system is that since the power source is gas pressure, you can implement that pressure somewhere besides the combustion chamber itself. Firing both actuators together moves the robot forward, while firing one side or the other can rotate the robot, providing some directional control.

“It took a lot of care, iterations, and intelligence to come up with this steerable, insect-scale robot,” Shepherd told us. “Does it have to have legs? No. It could be a speedy slug, or a flapping bee. The amplitudes and frequencies possible with this system allow for all of these possibilities. In fact, the real issue we have is making things move slowly.”

Getting these actuators to slow down a bit is one of the things that the researchers are looking at next. By trading speed for force, the idea is to make robots that can walk as well as run and jump. And of course finding a way to untether these systems is a natural next step. Some of the other stuff that they’re thinking about is pretty wild, as Shepherd tells us: “One idea we want to explore in the future is using aggregates of these small and powerful actuators as large, variable recruitment musculature in large robots. Putting thousands of these actuators in bundles over a rigid endoskeleton could allow for dexterous and fast land-based hybrid robots.” Personally, I’m having trouble even picturing a robot like that, but that’s what’s exciting about it, right? A large robot with muscles powered by thousands of tiny explosions—wow.

Powerful, soft combustion actuators for insect-scale robots, by Cameron A. Aubin, Ronald H. Heisser, Ofek Peretz, Julia Timko, Jacqueline Lo, E. Farrell Helbling, Sadaf Sobhani, Amir D. Gat, and Robert F. Shepherd from Cornell, is published in Science.

It’s hard to beat the energy density of chemical fuels. Batteries are quiet and clean and easy to integrate with electrically powered robots, but they’re 20 to 50 times less energy dense than a chemical fuel like methanol or butane. This is fine for most robots that can afford to just carry around a whole bunch of batteries, but as you start looking at robots that are insect-size or smaller, batteries simply don’t scale down very well. And it’s not just the batteries—electric actuators don’t scale down well either, especially if you’re looking for something that can generate a lot of power.

In a paper published 14 September in the journal Science, researchers from Cornell have tackled the small-scale actuation problem with what is essentially a very tiny, very soft internal-combustion engine. Methane vapor and oxygen are injected into a soft combustion chamber, where an itty-bitty li’l spark ignites the mixture. In half a millisecond, the top of the chamber balloons upward like a piston, generating forces of 9.5 newtons through a cycle that can repeat 100 times every second. Put two of these actuators together (driving two legs a piece) and you’ve got an exceptionally powerful soft quadruped robot.

Each of the two actuators powering this robot weighs just 325 milligrams and is about a quarter of the size of a U.S. penny. Part of the reason that they can be so small is that most of the associated components are off-board, including the fuel itself, the system that mixes and delivers the fuel, and the electrical source for the spark generator. But even without all of that stuff, the actuator has a bunch going on that enables it to operate continuously at high cycle frequencies without melting.

A view of the actuator and its component materials along with a diagram of the combustion actuation cycle.Science Robotics

The biggest issue may be that this actuator has to handle actual explosions, meaning that careful design is required to make sure that it doesn’t torch itself every time it goes off. The small combustion volume helps with this, as does the flame-resistant elastomer material and the integrated flame arrestor. Despite the violence inherent to how this actuator works, it’s actually very durable, and the researchers estimate that it can operate continuously for more than 750,000 cycles (8.5 hours at 50 hertz) without any drop in performance.

“What is interesting is just how powerful small-scale combustion is,” says Robert F. Shepherd, who runs the Organic Robotics Lab at Cornell. We covered some of Shepherd’s work on combustion-powered robots nearly a decade ago, with this weird pink jumping thing at IROS 2014. But going small has both challenges and benefits, Shepherd tells us. “We operate in the lower limit of what volumes of gases are combustible. It’s an interesting place for science, and the engineering outcomes are also useful.”

The first of those engineering outcomes is a little insect-scale quadrupedal robot that utilizes two of these soft combustion actuators to power a pair of legs each. The robot is 29 millimeters long and weighs just 1.6 grams, but it can jump a staggering 59 centimeters straight up and walk while carrying 22 times its own weight. For an insect-scale robot, Shepherd says, this is “near insect level performance, jumping extremely high, very quickly, and carrying large loads.”

Cornell University

It’s a little bit hard to see how the quadruped actually walks, since the actuators move so fast. Each actuator controls one side of the robot, with one combustion chamber connected to chambers at each foot with elastomer membranes. An advantage of this actuation system is that since the power source is gas pressure, you can implement that pressure somewhere besides the combustion chamber itself. Firing both actuators together moves the robot forward, while firing one side or the other can rotate the robot, providing some directional control.

“It took a lot of care, iterations, and intelligence to come up with this steerable, insect-scale robot,” Shepherd told us. “Does it have to have legs? No. It could be a speedy slug, or a flapping bee. The amplitudes and frequencies possible with this system allow for all of these possibilities. In fact, the real issue we have is making things move slowly.”

Getting these actuators to slow down a bit is one of the things that the researchers are looking at next. By trading speed for force, the idea is to make robots that can walk as well as run and jump. And of course finding a way to untether these systems is a natural next step. Some of the other stuff that they’re thinking about is pretty wild, as Shepherd tells us: “One idea we want to explore in the future is using aggregates of these small and powerful actuators as large, variable recruitment musculature in large robots. Putting thousands of these actuators in bundles over a rigid endoskeleton could allow for dexterous and fast land-based hybrid robots.” Personally, I’m having trouble even picturing a robot like that, but that’s what’s exciting about it, right? A large robot with muscles powered by thousands of tiny explosions—wow.

Powerful, soft combustion actuators for insect-scale robots, by Cameron A. Aubin, Ronald H. Heisser, Ofek Peretz, Julia Timko, Jacqueline Lo, E. Farrell Helbling, Sadaf Sobhani, Amir D. Gat, and Robert F. Shepherd from Cornell, is published in Science.

This sponsored article is brought to you by NYU Tandon School of Engineering.

To address today’s health challenges, especially in our aging society, we must become more intelligent in our approaches. Clinicians now have access to a range of advanced technologies designed to assist early diagnosis, evaluate prognosis, and enhance patient health outcomes, including telemedicine, medical robots, powered prosthetics, exoskeletons, and AI-powered smart wearables. However, many of these technologies are still in their infancy.

The belief that advancing technology can improve human health is central to research related to medical device technologies. This forms the heart of research for Prof. S. Farokh Atashzar who directs the Medical Robotics and Interactive Intelligent Technologies (MERIIT) Lab at the NYU Tandon School of Engineering.

Atashzar is an Assistant Professor of Electrical and Computer Engineering and Mechanical and Aerospace Engineering at NYU Tandon. He is also a member of NYU WIRELESS, a consortium of researchers dedicated to the next generation of wireless technology, as well as the Center for Urban Science and Progress (CUSP), a center of researchers dedicated to all things related to the future of modern urban life.

Atashzar’s work is dedicated to developing intelligent, interactive robotic, and AI-driven assistive machines that can augment human sensorimotor capabilities and allow our healthcare system to go beyond natural competences and overcome physiological and pathological barriers.

Stroke is the leading cause of age-related motor disabilities and is becoming more prevalent in younger populations as well. But while there is a burgeoning marketplace for rehabilitation devices that claim to accelerate recovery, including robotic rehabilitation systems, recommendations for how and when to use them are based mostly on subjective evaluation of the sensorimotor capacities of patients in need.

Atashzar is working in collaboration with John-Ross Rizzo, associate professor of Biomedical Engineering at NYU Tandon and Ilse Melamid Associate Professor of rehabilitation medicine at the NYU School of Medicine and Dr. Ramin Bighamian from the U.S. Food and Drug Administration to design a regulatory science tool (RST) based on data from biomarkers in order to improve the review processes for such devices and how best to use them. The team is designing and validating a robust recovery biomarker enabling a first-ever stroke rehabilitation RST based on exchanges between regions of the central and peripheral nervous systems.

S. Farokh Atashzar is an Assistant Professor of Electrical and Computer Engineering and Mechanical and Aerospace Engineering at New York University Tandon School of Engineering. He is also a member of NYU WIRELESS, a consortium of researchers dedicated to the next generation of wireless technology, as well as the Center for Urban Science and Progress (CUSP), a center of researchers dedicated to all things related to the future of modern urban life, and directs the MERIIT Lab at NYU Tandon.NYU Tandon

In addition, Atashzar is collaborating with Smita Rao, PT, the inaugural Robert S. Salant Endowed Associate Professor of Physical Therapy. Together, they aim to identify AI-driven computational biomarkers for motor control and musculoskeletal damage and to decode the hidden complex synergistic patterns of degraded muscle activation using data collected from surface electromyography (sEMG) and high-density sEMG. In the past few years, this collaborative effort has been exploring the fascinating world of “Nonlinear Functional Muscle Networks” — a new computational window (rooted in Shannon’s information theory) into human motor control and mobility. This synergistic network orchestrates the “music of mobility,” harmonizing the synchrony between muscles to facilitate fluid movement.

But rehabilitation is only one of the research thrusts at MERIIT lab. If you can prevent strokes from happening or reoccurring, you can head off the problem before it happens. For Atashzar, a big clue could be where you least expect it: in your retina.

Atashzar along with NYU Abu Dhabi Assistant Professor Farah Shamout, are working on a project they call “EyeScore,” an AI-powered technology that uses non-invasive scans of the retina to predict the recurrence of stroke in patients. They use optical coherence tomography — a scan of the back of the retina — and track changes over time using advanced deep learning models. The retina, attached directly to the brain through the optic nerve, can be used as a physiological window for changes in the brain itself.

Atashzar and Shamout are currently formulating their hybrid AI model, pinpointing the exact changes that can predict a stroke and recurrence of strokes. The outcome will be able to analyze these images and flag potentially troublesome developments. And since the scans are already in use in optometrist offices, this life-saving technology could be in the hands of medical professionals sooner than expected.

Atashzar is utilizing AI algorithms for uses beyond stroke. Like many researchers, his gaze was drawn to the largest medical event in recent history: COVID-19. In the throes of the COVID-19 pandemic, the very bedrock of global healthcare delivery was shaken. COVID-19 patients, susceptible to swift and severe deterioration, presented a serious problem for caregivers.

Especially in the pandemic’s early days, when our grasp of the virus was tenuous at best, predicting patient outcomes posed a formidable challenge. The merest tweaks in admission protocols held the power to dramatically shift patient fates, underscoring the need for vigilant monitoring. As healthcare systems groaned under the pandemic’s weight and contagion fears loomed, outpatient and nursing center residents were steered toward remote symptom tracking via telemedicine. This cautious approach sought to spare them unnecessary hospital exposure, allowing in-person visits only for those in the throes of grave symptoms.

But while much of the pandemic’s research spotlight fell on diagnosing COVID-19, this study took a different avenue: predicting patient deterioration in the future. Existing studies often juggled an array of data inputs, from complex imaging to lab results, but failed to harness data’s temporal aspects. Enter this research, which prioritized simplicity and scalability, leaning on data easily gathered not only within medical walls but also in the comfort of patients’ homes with the use of simple wearables.

S. Farokh Atashzar and colleagues at NYU Tandon are using deep neural network models to assess COVID data and try to predict patient deterioration in the future.

Atashzar, along with his Co-PI of the project Yao Wang, Professor of Biomedical Engineering and Electrical and Computer Engineering at NYU Tandon, used a novel deep neural network model to assess COVID data, leveraging time series data on just three vital signs to foresee COVID-19 patient deterioration for some 37,000 patients. The ultimate prize? A streamlined predictive model capable of aiding clinical decision-making for a wide spectrum of patients. Oxygen levels, heartbeats, and temperatures formed the trio of vital signs under scrutiny, a choice propelled by the ubiquity of wearable tech like smartwatches. A calculated exclusion of certain signs, like blood pressure, followed, due to their incompatibility with these wearables.

The researchers utilized real-world data from NYU Langone Health’s archives spanning January 2020 to September 2022 lent authenticity. Predicting deterioration within timeframes of 3 to 24 hours, the model analyzed vital sign data from the preceding 24 hours. This crystal ball aimed to forecast outcomes ranging from in-hospital mortality to intensive care unit admissions or intubations.

“In a situation where a hospital is overloaded, getting a CT scan for every single patient would be very difficult or impossible, especially in remote areas when the healthcare system is overstretched,” says Atashzar. “So we are minimizing the need for data, while at the same time, maximizing the accuracy for prediction. And that can help with creating better healthcare access in remote areas and in areas with limited healthcare.”

In addition to addressing the pandemic at the micro level (individuals), Atashzar and his team are also working on algorithmic solutions that can assist the healthcare system at the meso and macro level. In another effort related to COVID-19, Atashzar and his team are developing novel probabilistic models that can better predict the spread of disease when taking into account the effects of vaccination and mutation of the virus. Their efforts go beyond the classic small-scale models that were previously used for small epidemics. They are working on these large-scale complex models in order to help governments better prepare for pandemics and mitigate rapid disease spread. Atashzar is drawing inspiration from his active work with control algorithms used in complex networks of robotic systems. His team is now utilizing similar techniques to develop new algorithmic tools for controlling spread in the networked dynamic models of human society.

A state-of-the-art human-machine interface module with wearable controller is one of many multi-modal technologies tested in S. Farokh Atashzar’s MERIIT Lab at NYU Tandon.NYU Tandon

These projects represent only a fraction of Atashzar’s work. In the MERIIT lab, he and his students build cyber-physical systems that augment the functionality of the next-generation medical robotic systems. They delve into haptics and robotics for a wide range of medical applications. Examples include telesurgery and telerobotic rehabilitation, which are built upon the capabilities of next-generation telecommunications. The team is specifically interested in the application of 5G-based tactile internet in medical robotics.

Recently, he received a donation from the Intuitive Foundation: a Da Vinci research kit. This state-of-the-art surgical system will allow his team to explore ways for a surgeon in one location to operate on a patient in another—whether they are in a different city, region, or even continent. While several researchers have investigated this vision in the past decade, Atashzar is specifically concentrating on connecting the power of the surgeon’s mind with the autonomy of surgical robots - promoting discussions on ways to share the surgical autonomy between the intelligence of machines and the mind of surgeons. This approach aims to reduce mental fatigue and cognitive load on surgeons while reintroducing the sense of haptics lost in traditional surgical robotic systems.

Atashzar poses with NYU Tandon’s Da Vinci research kit. This state-of-the-art surgical system will allow his team to explore ways for a surgeon in one location to operate on a patient in another—whether they are in a different city, region, or even continent.NYU Tandon

In a related line of research, the MERIIT lab is also focusing on cutting-edge human-machine interface technologies that enable neuro-to-device capabilities. These technologies have direct applications in exoskeletal devices, next-generation prosthetics, rehabilitation robots, and possibly the upcoming wave of augmented reality systems in our smart and connected society. One common significant challenge of such systems which is focused by the team is predicting the intended actions of the human users through processing signals generated by functional behavior of motor neurons.

By solving this challenge using advanced AI modules in real-time, the team can decode a user’s motor intentions and predict the intended gestures for controlling robots and virtual reality systems in an agile and robust manner. Some practical challenges include ensuring the generalizability, scalability, and robustness of these AI-driven solutions, given the variability of human neurophysiology and heavy reliance of classic models on data. Powered by such predictive models, the team is advancing the complex control of human-centric machines and robots. They are also crafting algorithms that take into account human physiology and biomechanics. This requires conducting transdisciplinary solutions bridging AI and nonlinear control theories.

Atashzar’s work dovetails perfectly with the work of other researchers at NYU Tandon, which prizes interdisciplinary work without the silos of traditional departments.

“Dr. Atashzar shines brightly in the realm of haptics for telerobotic medical procedures, positioning him as a rising star in his research community,” says Katsuo Kurabayashi, the new chair of the Mechanical and Aerospace Engineering department at NYU Tandon. “His pioneering research carries the exciting potential to revolutionize rehabilitation therapy, facilitate the diagnosis of neuromuscular diseases, and elevate the field of surgery. This holds the key to ushering in a new era of sophisticated remote human-machine interactions and leveraging machine learning-driven sensor signal interpretations.”

This commitment to human health, through the embrace of new advances in biosignals, robotics, and rehabilitation, is at the heart of Atashzar’s enduring work, and his unconventional approaches to age-old problem make him a perfect example of the approach to engineering embraced at NYU Tandon.

This sponsored article is brought to you by NYU Tandon School of Engineering.

To address today’s health challenges, especially in our aging society, we must become more intelligent in our approaches. Clinicians now have access to a range of advanced technologies designed to assist early diagnosis, evaluate prognosis, and enhance patient health outcomes, including telemedicine, medical robots, powered prosthetics, exoskeletons, and AI-powered smart wearables. However, many of these technologies are still in their infancy.

The belief that advancing technology can improve human health is central to research related to medical device technologies. This forms the heart of research for Prof. S. Farokh Atashzar who directs the Medical Robotics and Interactive Intelligent Technologies (MERIIT) Lab at the NYU Tandon School of Engineering.

Atashzar is an Assistant Professor of Electrical and Computer Engineering and Mechanical and Aerospace Engineering at NYU Tandon. He is also a member of NYU WIRELESS, a consortium of researchers dedicated to the next generation of wireless technology, as well as the Center for Urban Science and Progress (CUSP), a center of researchers dedicated to all things related to the future of modern urban life.

Atashzar’s work is dedicated to developing intelligent, interactive robotic, and AI-driven assistive machines that can augment human sensorimotor capabilities and allow our healthcare system to go beyond natural competences and overcome physiological and pathological barriers.

Stroke is the leading cause of age-related motor disabilities and is becoming more prevalent in younger populations as well. But while there is a burgeoning marketplace for rehabilitation devices that claim to accelerate recovery, including robotic rehabilitation systems, recommendations for how and when to use them are based mostly on subjective evaluation of the sensorimotor capacities of patients in need.

Atashzar is working in collaboration with John-Ross Rizzo, associate professor of Biomedical Engineering at NYU Tandon and Ilse Melamid Associate Professor of rehabilitation medicine at the NYU School of Medicine and Dr. Ramin Bighamian from the U.S. Food and Drug Administration to design a regulatory science tool (RST) based on data from biomarkers in order to improve the review processes for such devices and how best to use them. The team is designing and validating a robust recovery biomarker enabling a first-ever stroke rehabilitation RST based on exchanges between regions of the central and peripheral nervous systems.

S. Farokh Atashzar is an Assistant Professor of Electrical and Computer Engineering and Mechanical and Aerospace Engineering at New York University Tandon School of Engineering. He is also a member of NYU WIRELESS, a consortium of researchers dedicated to the next generation of wireless technology, as well as the Center for Urban Science and Progress (CUSP), a center of researchers dedicated to all things related to the future of modern urban life, and directs the MERIIT Lab at NYU Tandon.NYU Tandon

In addition, Atashzar is collaborating with Smita Rao, PT, the inaugural Robert S. Salant Endowed Associate Professor of Physical Therapy. Together, they aim to identify AI-driven computational biomarkers for motor control and musculoskeletal damage and to decode the hidden complex synergistic patterns of degraded muscle activation using data collected from surface electromyography (sEMG) and high-density sEMG. In the past few years, this collaborative effort has been exploring the fascinating world of “Nonlinear Functional Muscle Networks” — a new computational window (rooted in Shannon’s information theory) into human motor control and mobility. This synergistic network orchestrates the “music of mobility,” harmonizing the synchrony between muscles to facilitate fluid movement.

But rehabilitation is only one of the research thrusts at MERIIT lab. If you can prevent strokes from happening or reoccurring, you can head off the problem before it happens. For Atashzar, a big clue could be where you least expect it: in your retina.

Atashzar along with NYU Abu Dhabi Assistant Professor Farah Shamout, are working on a project they call “EyeScore,” an AI-powered technology that uses non-invasive scans of the retina to predict the recurrence of stroke in patients. They use optical coherence tomography — a scan of the back of the retina — and track changes over time using advanced deep learning models. The retina, attached directly to the brain through the optic nerve, can be used as a physiological window for changes in the brain itself.

Atashzar and Shamout are currently formulating their hybrid AI model, pinpointing the exact changes that can predict a stroke and recurrence of strokes. The outcome will be able to analyze these images and flag potentially troublesome developments. And since the scans are already in use in optometrist offices, this life-saving technology could be in the hands of medical professionals sooner than expected.

Atashzar is utilizing AI algorithms for uses beyond stroke. Like many researchers, his gaze was drawn to the largest medical event in recent history: COVID-19. In the throes of the COVID-19 pandemic, the very bedrock of global healthcare delivery was shaken. COVID-19 patients, susceptible to swift and severe deterioration, presented a serious problem for caregivers.

Especially in the pandemic’s early days, when our grasp of the virus was tenuous at best, predicting patient outcomes posed a formidable challenge. The merest tweaks in admission protocols held the power to dramatically shift patient fates, underscoring the need for vigilant monitoring. As healthcare systems groaned under the pandemic’s weight and contagion fears loomed, outpatient and nursing center residents were steered toward remote symptom tracking via telemedicine. This cautious approach sought to spare them unnecessary hospital exposure, allowing in-person visits only for those in the throes of grave symptoms.

But while much of the pandemic’s research spotlight fell on diagnosing COVID-19, this study took a different avenue: predicting patient deterioration in the future. Existing studies often juggled an array of data inputs, from complex imaging to lab results, but failed to harness data’s temporal aspects. Enter this research, which prioritized simplicity and scalability, leaning on data easily gathered not only within medical walls but also in the comfort of patients’ homes with the use of simple wearables.

S. Farokh Atashzar and colleagues at NYU Tandon are using deep neural network models to assess COVID data and try to predict patient deterioration in the future.

Atashzar, along with his Co-PI of the project Yao Wang, Professor of Biomedical Engineering and Electrical and Computer Engineering at NYU Tandon, used a novel deep neural network model to assess COVID data, leveraging time series data on just three vital signs to foresee COVID-19 patient deterioration for some 37,000 patients. The ultimate prize? A streamlined predictive model capable of aiding clinical decision-making for a wide spectrum of patients. Oxygen levels, heartbeats, and temperatures formed the trio of vital signs under scrutiny, a choice propelled by the ubiquity of wearable tech like smartwatches. A calculated exclusion of certain signs, like blood pressure, followed, due to their incompatibility with these wearables.

The researchers utilized real-world data from NYU Langone Health’s archives spanning January 2020 to September 2022 lent authenticity. Predicting deterioration within timeframes of 3 to 24 hours, the model analyzed vital sign data from the preceding 24 hours. This crystal ball aimed to forecast outcomes ranging from in-hospital mortality to intensive care unit admissions or intubations.

“In a situation where a hospital is overloaded, getting a CT scan for every single patient would be very difficult or impossible, especially in remote areas when the healthcare system is overstretched,” says Atashzar. “So we are minimizing the need for data, while at the same time, maximizing the accuracy for prediction. And that can help with creating better healthcare access in remote areas and in areas with limited healthcare.”

In addition to addressing the pandemic at the micro level (individuals), Atashzar and his team are also working on algorithmic solutions that can assist the healthcare system at the meso and macro level. In another effort related to COVID-19, Atashzar and his team are developing novel probabilistic models that can better predict the spread of disease when taking into account the effects of vaccination and mutation of the virus. Their efforts go beyond the classic small-scale models that were previously used for small epidemics. They are working on these large-scale complex models in order to help governments better prepare for pandemics and mitigate rapid disease spread. Atashzar is drawing inspiration from his active work with control algorithms used in complex networks of robotic systems. His team is now utilizing similar techniques to develop new algorithmic tools for controlling spread in the networked dynamic models of human society.

A state-of-the-art human-machine interface module with wearable controller is one of many multi-modal technologies tested in S. Farokh Atashzar’s MERIIT Lab at NYU Tandon.NYU Tandon

These projects represent only a fraction of Atashzar’s work. In the MERIIT lab, he and his students build cyber-physical systems that augment the functionality of the next-generation medical robotic systems. They delve into haptics and robotics for a wide range of medical applications. Examples include telesurgery and telerobotic rehabilitation, which are built upon the capabilities of next-generation telecommunications. The team is specifically interested in the application of 5G-based tactile internet in medical robotics.

Recently, he received a donation from the Intuitive Foundation: a Da Vinci research kit. This state-of-the-art surgical system will allow his team to explore ways for a surgeon in one location to operate on a patient in another—whether they are in a different city, region, or even continent. While several researchers have investigated this vision in the past decade, Atashzar is specifically concentrating on connecting the power of the surgeon’s mind with the autonomy of surgical robots - promoting discussions on ways to share the surgical autonomy between the intelligence of machines and the mind of surgeons. This approach aims to reduce mental fatigue and cognitive load on surgeons while reintroducing the sense of haptics lost in traditional surgical robotic systems.

Atashzar poses with NYU Tandon’s Da Vinci research kit. This state-of-the-art surgical system will allow his team to explore ways for a surgeon in one location to operate on a patient in another—whether they are in a different city, region, or even continent.NYU Tandon

In a related line of research, the MERIIT lab is also focusing on cutting-edge human-machine interface technologies that enable neuro-to-device capabilities. These technologies have direct applications in exoskeletal devices, next-generation prosthetics, rehabilitation robots, and possibly the upcoming wave of augmented reality systems in our smart and connected society. One common significant challenge of such systems which is focused by the team is predicting the intended actions of the human users through processing signals generated by functional behavior of motor neurons.

By solving this challenge using advanced AI modules in real-time, the team can decode a user’s motor intentions and predict the intended gestures for controlling robots and virtual reality systems in an agile and robust manner. Some practical challenges include ensuring the generalizability, scalability, and robustness of these AI-driven solutions, given the variability of human neurophysiology and heavy reliance of classic models on data. Powered by such predictive models, the team is advancing the complex control of human-centric machines and robots. They are also crafting algorithms that take into account human physiology and biomechanics. This requires conducting transdisciplinary solutions bridging AI and nonlinear control theories.

Atashzar’s work dovetails perfectly with the work of other researchers at NYU Tandon, which prizes interdisciplinary work without the silos of traditional departments.

“Dr. Atashzar shines brightly in the realm of haptics for telerobotic medical procedures, positioning him as a rising star in his research community,” says Katsuo Kurabayashi, the new chair of the Mechanical and Aerospace Engineering department at NYU Tandon. “His pioneering research carries the exciting potential to revolutionize rehabilitation therapy, facilitate the diagnosis of neuromuscular diseases, and elevate the field of surgery. This holds the key to ushering in a new era of sophisticated remote human-machine interactions and leveraging machine learning-driven sensor signal interpretations.”

This commitment to human health, through the embrace of new advances in biosignals, robotics, and rehabilitation, is at the heart of Atashzar’s enduring work, and his unconventional approaches to age-old problem make him a perfect example of the approach to engineering embraced at NYU Tandon.

This paper focuses on the topic of “everyday life” as it is addressed in Human-Robot Interaction (HRI) research. It starts from the argument that while human daily life with social robots has been increasingly discussed and studied in HRI, the concept of everyday life lacks clarity or systematic analysis, and it plays only a secondary role in supporting the study of the key HRI topics. In order to help conceptualise everyday life as a research theme in HRI in its own right, we provide an overview of the Social Science and Humanities (SSH) perspectives on everyday life and lived experiences, particularly in sociology, and identify the key elements that may serve to further develop and empirically study such a concept in HRI. We propose new angles of analysis that may help better explore unique aspects of human engagement with social robots. We look at the everyday not just as a reality as we know it (i.e., the realm of the “ordinary”) but also as the future that we need to envision and strive to materialise (i.e., the transformation that will take place through the “extraordinary” that comes with social robots). Finally, we argue that HRI research would benefit not only from engaging with a systematic conceptualisation but also critique of the contemporary everyday life with social robots. This is how HRI studies could play an important role in challenging the current ways of understanding of what makes different aspects of the human world “natural” and ultimately help bringing a social change towards what we consider a “good life.”

Abdominal palpation is one of the basic but important physical examination methods used by physicians. Visual, auditory, and haptic feedback from the patients are known to be the main sources of feedback they use in the diagnosis. However, learning to interpret this feedback and making accurate diagnosis require several years of training. Many abdominal palpation training simulators have been proposed to date, but very limited attempts have been reported in integrating vocal pain expressions into physical abdominal palpation simulators. Here, we present a vocal pain expression augmentation for a robopatient. The proposed robopatient is capable of providing real-time facial and vocal pain expressions based on the exerted palpation force and position on the abdominal phantom of the robopatient. A pilot study is conducted to test the proposed system, and we show the potential of integrating vocal pain expressions to the robopatient. The platform has also been tested by two clinical experts with prior experience in abdominal palpation. Their evaluations on functionality and suggestions for improvements are presented. We highlight the advantages of the proposed robopatient with real-time vocal and facial pain expressions as a controllable simulator platform for abdominal palpation training studies. Finally, we discuss the limitations of the proposed approach and suggest several future directions for improvements.

Introduction: Handwriting is a complex task that requires coordination of motor, sensory, cognitive, memory, and linguistic skills to master. The extent these processes are involved depends on the complexity of the handwriting task. Evaluating the difficulty of a handwriting task is a challenging problem since it relies on subjective judgment of experts.

Methods: In this paper, we propose a machine learning approach for evaluating the difficulty level of handwriting tasks. We propose two convolutional neural network (CNN) models for single- and multilabel classification where single-label classification is based on the mean of expert evaluation while the multilabel classification predicts the distribution of experts’ assessment. The models are trained with a dataset containing 117 spatio-temporal features from the stylus and hand kinematics, which are recorded for all letters of the Arabic alphabet.

Results: While single- and multilabel classification models achieve decent accuracy (96% and 88% respectively) using all features, the hand kinematics features do not significantly influence the performance of the models.

Discussion: The proposed models are capable of extracting meaningful features from the handwriting samples and predicting their difficulty levels accurately. The proposed approach has the potential to be used to personalize handwriting learning tools and provide automatic evaluation of the quality of handwriting.