Tag Archives: camera

#439110 Robotic Exoskeletons Could One Day Walk ...

Engineers, using artificial intelligence and wearable cameras, now aim to help robotic exoskeletons walk by themselves.

Increasingly, researchers around the world are developing lower-body exoskeletons to help people walk. These are essentially walking robots users can strap to their legs to help them move.

One problem with such exoskeletons: They often depend on manual controls to switch from one mode of locomotion to another, such as from sitting to standing, or standing to walking, or walking on the ground to walking up or down stairs. Relying on joysticks or smartphone apps every time you want to switch the way you want to move can prove awkward and mentally taxing, says Brokoslaw Laschowski, a robotics researcher at the University of Waterloo in Canada.

Scientists are working on automated ways to help exoskeletons recognize when to switch locomotion modes — for instance, using sensors attached to legs that can detect bioelectric signals sent from your brain to your muscles telling them to move. However, this approach comes with a number of challenges, such as how how skin conductivity can change as a person’s skin gets sweatier or dries off.

Now several research groups are experimenting with a new approach: fitting exoskeleton users with wearable cameras to provide the machines with vision data that will let them operate autonomously. Artificial intelligence (AI) software can analyze this data to recognize stairs, doors, and other features of the surrounding environment and calculate how best to respond.

Laschowski leads the ExoNet project, the first open-source database of high-resolution wearable camera images of human locomotion scenarios. It holds more than 5.6 million images of indoor and outdoor real-world walking environments. The team used this data to train deep-learning algorithms; their convolutional neural networks can already automatically recognize different walking environments with 73 percent accuracy “despite the large variance in different surfaces and objects sensed by the wearable camera,” Laschowski notes.

According to Laschowski, a potential limitation of their work their reliance on conventional 2-D images, whereas depth cameras could also capture potentially useful distance data. He and his collaborators ultimately chose not to rely on depth cameras for a number of reasons, including the fact that the accuracy of depth measurements typically degrades in outdoor lighting and with increasing distance, he says.

In similar work, researchers in North Carolina had volunteers with cameras either mounted on their eyeglasses or strapped onto their knees walk through a variety of indoor and outdoor settings to capture the kind of image data exoskeletons might use to see the world around them. The aim? “To automate motion,” says Edgar Lobaton an electrical engineering researcher at North Carolina State University. He says they are focusing on how AI software might reduce uncertainty due to factors such as motion blur or overexposed images “to ensure safe operation. We want to ensure that we can really rely on the vision and AI portion before integrating it into the hardware.”

In the future, Laschowski and his colleagues will focus on improving the accuracy of their environmental analysis software with low computational and memory storage requirements, which are important for onboard, real-time operations on robotic exoskeletons. Lobaton and his team also seek to account for uncertainty introduced into their visual systems by movements .

Ultimately, the ExoNet researchers want to explore how AI software can transmit commands to exoskeletons so they can perform tasks such as climbing stairs or avoiding obstacles based on a system’s analysis of a user's current movements and the upcoming terrain. With autonomous cars as inspiration, they are seeking to develop autonomous exoskeletons that can handle the walking task without human input, Laschowski says.

However, Laschowski adds, “User safety is of the utmost importance, especially considering that we're working with individuals with mobility impairments,” resulting perhaps from advanced age or physical disabilities.
“The exoskeleton user will always have the ability to override the system should the classification algorithm or controller make a wrong decision.” Continue reading

Posted in Human Robots

#439100 Video Friday: Robotic Eyeball Camera

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

RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
RoboCup 2021 – June 22-28, 2021 – [Online Event]
DARPA SubT Finals – September 21-23, 2021 – Louisville, KY, USA
WeRobot 2021 – September 23-25, 2021 – Coral Gables, FL, USA
Let us know if you have suggestions for next week, and enjoy today's videos.

What if seeing devices looked like us? Eyecam is a prototype exploring the potential future design of sensing devices. Eyecam is a webcam shaped like a human eye that can see, blink, look around and observe us.

And it's open source, so you can build your own!

[ Eyecam ]

Looks like Festo will be turning some of its bionic robots into educational kits, which is a pretty cool idea.

[ Bionics4Education ]

Underwater soft robots are challenging to model and control because of their high degrees of freedom and their intricate coupling with water. In this paper, we present a method that leverages the recent development in differentiable simulation coupled with a differentiable, analytical hydrodynamic model to assist with the modeling and control of an underwater soft robot. We apply this method to Starfish, a customized soft robot design that is easy to fabricate and intuitive to manipulate.

[ MIT CSAIL ]

Rainbow Robotics, the company who made HUBO, has a new collaborative robot arm.

[ Rainbow Robotics ]

Thanks Fan!

We develop an integrated robotic platform for advanced collaborative robots and demonstrates an application of multiple robots collaboratively transporting an object to different positions in a factory environment. The proposed platform integrates a drone, a mobile manipulator robot, and a dual-arm robot to work autonomously, while also collaborating with a human worker. The platform also demonstrates the potential of a novel manufacturing process, which incorporates adaptive and collaborative intelligence to improve the efficiency of mass customization for the factory of the future.

[ Paper ]

Thanks Poramate!

In Sevastopol State University the team of the Laboratory of Underwater Robotics and Control Systems and Research and Production Association “Android Technika” performed tests of an underwater anropomorphic manipulator robot.

[ Sevastopol State ]

Thanks Fan!

Taiwanese company TCI Gene created a COVID test system based on their fully automated and enclosed gene testing machine QVS-96S. The system includes two ABB robots and carries out 1800 tests per day, operating 24/7. Every hour 96 virus samples tests are made with an accuracy of 99.99%.

[ ABB ]

A short video showing how a Halodi Robotics can be used in a commercial guarding application.

[ Halodi ]

During the past five years, under the NASA Early Space Innovations program, we have been developing new design optimization methods for underactuated robot hands, aiming to achieve versatile manipulation in highly constrained environments. We have prototyped hands for NASA’s Astrobee robot, an in-orbit assistive free flyer for the International Space Station.

[ ROAM Lab ]

The new, improved OTTO 1500 is a workhorse AMR designed to move heavy payloads through demanding environments faster than any other AMR on the market, with zero compromise to safety.

[ ROAM Lab ]

Very, very high performance sensing and actuation to pull this off.

[ Ishikawa Group ]

We introduce a conversational social robot designed for long-term in-home use to help with loneliness. We present a novel robot behavior design to have simple self-reflection conversations with people to improve wellness, while still being feasible, deployable, and safe.

[ HCI Lab ]

We are one of the 5 winners of the Start-up Challenge. This video illustrates what we achieved during the Swisscom 5G exploration week. Our proof-of-concept tele-excavation system is composed of a Menzi Muck M545 walking excavator automated & customized by Robotic Systems Lab and IBEX motion platform as the operator station. The operator and remote machine are connected for the first time via a 5G network infrastructure which was brought to our test field by Swisscom.

[ RSL ]

This video shows LOLA balancing on different terrain when being pushed in different directions. The robot is technically blind, not using any camera-based or prior information on the terrain (hard ground is assumed).

[ TUM ]

Autonomous driving when you cannot see the road at all because it's buried in snow is some serious autonomous driving.

[ Norlab ]

A hierarchical and robust framework for learning bipedal locomotion is presented and successfully implemented on the 3D biped robot Digit. The feasibility of the method is demonstrated by successfully transferring the learned policy in simulation to the Digit robot hardware, realizing sustained walking gaits under external force disturbances and challenging terrains not included during the training process.

[ OSU ]

This is a video summary of the Center for Robot-Assisted Search and Rescue's deployments under the direction of emergency response agencies to more than 30 disasters in five countries from 2001 (9/11 World Trade Center) to 2018 (Hurricane Michael). It includes the first use of ground robots for a disaster (WTC, 2001), the first use of small unmanned aerial systems (Hurricane Katrina 2005), and the first use of water surface vehicles (Hurricane Wilma, 2005).

[ CRASAR ]

In March, a team from the Oxford Robotics Institute collected a week of epic off-road driving data, as part of the Sense-Assess-eXplain (SAX) project.

[ Oxford Robotics ]

As a part of the AAAI 2021 Spring Symposium Series, HEBI Robotics was invited to present an Industry Talk on the symposium's topic: Machine Learning for Mobile Robot Navigation in the Wild. Included in this presentation was a short case study on one of our upcoming mobile robots that is being designed to successfully navigate unstructured environments where today's robots struggle.

[ HEBI Robotics ]

Thanks Hardik!

This Lockheed Martin Robotics Seminar is from Chad Jenkins at the University of Michigan, on “Semantic Robot Programming… and Maybe Making the World a Better Place.”

I will present our efforts towards accessible and general methods of robot programming from the demonstrations of human users. Our recent work has focused on Semantic Robot Programming (SRP), a declarative paradigm for robot programming by demonstration that builds on semantic mapping. In contrast to procedural methods for motion imitation in configuration space, SRP is suited to generalize user demonstrations of goal scenes in workspace, such as for manipulation in cluttered environments. SRP extends our efforts to crowdsource robot learning from demonstration at scale through messaging protocols suited to web/cloud robotics. With such scaling of robotics in mind, prospects for cultivating both equal opportunity and technological excellence will be discussed in the context of broadening and strengthening Title IX and Title VI.

[ UMD ] Continue reading

Posted in Human Robots

#439089 Ingenuity’s Chief Pilot Explains How ...

On April 11, the Mars helicopter Ingenuity will take to the skies of Mars for the first time. It will do so fully autonomously, out of necessity—the time delay between Ingenuity’s pilots at the Jet Propulsion Laboratory and Jezero Crater on Mars makes manual or even supervisory control impossible. So the best that the folks at JPL can do is practice as much as they can in simulation, and then hope that the helicopter can handle everything on its own.

Here on Earth, simulation is a critical tool for many robotics applications, because it doesn’t rely on access to expensive hardware, is non-destructive, and can be run in parallel and at faster-than-real-time speeds to focus on solving specific problems. Once you think you’ve gotten everything figured out in simulation, you can always give it a try on the real robot and see how close you came. If it works in real life, great! And if not, well, you can tweak some stuff in the simulation and try again.

For the Mars helicopter, simulation is much more important, and much higher stakes. Testing the Mars helicopter under conditions matching what it’ll find on Mars is not physically possible on Earth. JPL has flown engineering models in Martian atmospheric conditions, and they’ve used an actuated tether to mimic Mars gravity, but there’s just no way to know what it’ll be like flying on Mars until they’ve actually flown on Mars. With that in mind, the Ingenuity team has been relying heavily on simulation, since that’s one of the best tools they have to prepare for their Martian flights. We talk with Ingenuity’s Chief Pilot, Håvard Grip, to learn how it all works.

Ingenuity Facts:
Body Size: a box of tissues

Brains: Qualcomm Snapdragon 801

Weight: 1.8 kilograms

Propulsion: Two 1.2m carbon fiber rotors

Navigation sensors: VGA camera, laser altimeter, inclinometer

Ingenuity is scheduled to make its first flight no earlier than April 11. Before liftoff, the Ingenuity team will conduct a variety of pre-flight checks, including verifying the responsiveness of the control system and spinning the blades up to full speed (2,537 rpm) without lifting off. If everything looks good, the first flight will consist of a 1 meter per second climb to 3 meters, 30 seconds of hover at 3 meters while rotating in place a bit, and then a descent to landing. If Ingenuity pulls this off, that will have made its entire mission a success. There will be more flights over the next few weeks, but all it takes is one to prove that autonomous helicopter flight on Mars is possible.

Last month, we spoke with Mars Helicopter Operations Lead Tim Canham about Ingenuity’s hardware, software, and autonomy, but we wanted to know more about how the Ingenuity team has been using simulation for everything from vehicle design to flight planning. To answer our questions, we talked with JPL’s Håvard Grip, who led the development of Ingenuity’s navigation and flight control systems. Grip also has the title of Ingenuity Chief Pilot, which is pretty awesome. He summarizes this role as “operating the flight control system to make the helicopter do what we want it to do.”

IEEE Spectrum: Can you tell me about the simulation environment that JPL uses for Ingenuity’s flight planning?

Håvard Grip: We developed a Mars helicopter simulation ourselves at JPL, based on a multi-body simulation framework that’s also developed at JPL, called DARTS/DSHELL. That's a system that has been in development at JPL for about 30 years now, and it's been used in a number of missions. And so we took that multibody simulation framework, and based on it we built our own Mars helicopter simulation, put together our own rotor model, our own aerodynamics models, and everything else that's needed in order to simulate a helicopter. We also had a lot of help from the rotorcraft experts at NASA Ames and NASA Langley.

Image: NASA/JPL

Ingenuity in JPL’s flight simulator.

Without being able to test on Mars, how much validation are you able to do of what you’re seeing in simulation?

We can do a fair amount, but it requires a lot of planning. When we made our first real prototype (with a full-size rotor that looked like what we were thinking of putting on Mars) we first spent a lot of time designing it and using simulation tools to guide that design, and when we were sufficiently confident that we were close enough, and that we understood enough about it, then we actually built the thing and designed a whole suite of tests in a vacuum chamber where where we could replicate Mars atmospheric conditions. And those tests were before we tried to fly the helicopter—they were specifically targeted at what we call system identification, which has to do with figuring out what the true properties, the true dynamics of a system are, compared to what we assumed in our models. So then we got to see how well our models did, and in the places where they needed adjustment, we could go back and do that.

The simulation work that we really started after that very first initial lift test, that’s what allowed us to unlock all of the secrets to building a helicopter that can fly on Mars.
—Håvard Grip, Ingenuity Chief Pilot

We did a lot of this kind of testing. It was a big campaign, in several stages. But there are of course things that you can't fully replicate, and you do depend on simulation to tie things together. For example, we can't truly replicate Martian gravity on Earth. We can replicate the atmosphere, but not the gravity, and so we have to do various things when we fly—either make the helicopter very light, or we have to help it a little bit by pulling up on it with a string to offload some of the weight. These things don't fully replicate what it will be like on Mars. We also can't simultaneously replicate the Mars aerodynamic environment and the physical and visual surroundings that the helicopter will be flying in. These are places where simulation tools definitely come in handy, with the ability to do full flight tests from A to B, with the helicopter taking off from the ground, running the flight software that it will be running on board, simulating the images that the navigation camera takes of the ground below as it flies, feeding that back into the flight software, and then controlling it.

To what extent can simulation really compensate for the kinds of physical testing that you can’t do on Earth?

It gives you a few different possibilities. We can take certain tests on Earth where we replicate key elements of the environment, like the atmosphere or the visual surroundings for example, and you can validate your simulation on those parameters that you can test on Earth. Then, you can combine those things in simulation, which gives you the ability to set up arbitrary scenarios and do lots and lots of tests. We can Monte Carlo things, we can do a flight a thousand times in a row, with small perturbations of various parameters and tease out what our sensitivities are to those things. And those are the kinds of things that you can't do with physical tests, both because you can't fully replicate the environment and also because of the resources that would be required to do the same thing a thousand times in a row.

Because there are limits to the physical testing we can do on Earth, there are elements where we know there's more uncertainty. On those aspects where the uncertainty is high, we tried to build in enough margin that we can handle a range of things. And simulation gives you the ability to then maybe play with those parameters, and put them at their outer limits, and test them beyond where the real parameters are going to be to make sure that you have robustness even in those extreme cases.

How do you make sure you’re not relying on simulation too much, especially since in some ways it’s your only option?

It’s about anchoring it in real data, and we’ve done a lot of that with our physical testing. I think what you’re referring to is making your simulation too perfect, and we’re careful to model the things that matter. For example, the simulated sensors that we use have realistic levels of simulated noise and bias in them, the navigation camera images have realistic levels of degradation, we have realistic disturbances from wind gusts. If you don’t properly account for those things, then you’re missing important details. So, we try to be as accurate as we can, and to capture that by overbounding in areas where we have a high degree of uncertainty.

What kinds of simulated challenges have you put the Mars helicopter through, and how do you decide how far to push those challenges?

One example is that we can simulate going over rougher terrain. We can push that, and see how far we can go and still have the helicopter behave the way that we want it to. Or we can inject levels of noise that maybe the real sensors don't see, but you want to just see how far you can push things and make sure that it's still robust.

Where we put the limits on this and what we consider to be realistic is often a challenge. We consider this on a case by case basis—if you have a sensor that you're dealing with, you try to do testing with it to characterize it and understand its performance as much as possible, and you build a level of confidence in it that allows you to find the proper balance.

When it comes to things like terrain roughness, it's a little bit of a different thing, because we're actually picking where we're flying the helicopter. We have made that choice, and we know what the terrain looks like around us, so we don’t have to wonder about that anymore.

Image: NASA/JPL-Caltech/University of Arizona

Satellite image of the Ingenuity flight area.

The way that we’re trying to approach this operationally is that we should be done with the engineering at this point. We’re not depending on going back and resimulating things, other than a few checks here and there.

Are there any examples of things you learned as part of the simulation process that resulted in changes to the hardware or mission?

You know, it’s been a journey. One of the early things that we discovered as part of modeling the helicopter was that the rotor dynamics were quite different for a helicopter on Mars, in particular with respect to how the rotor responds to the up and down bending of the blades because they’re not perfectly rigid. That motion is a very important influence on the overall flight dynamics of the helicopter, and what we discovered as we started modeling was that this motion is damped much less on Mars. Under-damped oscillatory things like that, you kind of figure might pose a control issue, and that is the case here: if you just naively design it as you might a helicopter on Earth, without taking this into account, you could have a system where the response to control inputs becomes very sluggish. So that required changes to the vehicle design from some of the very early concepts, and it led us to make a rotor that’s extremely light and rigid.

The design cycle for the Mars helicopter—it’s not like we could just build something and take it out to the back yard and try it and then come back and tweak it if it doesn’t work. It’s a much bigger effort to build something and develop a test program where you have to use a vacuum chamber to test it. So you really want to get as close as possible up front, on your first iteration, and not have to go back to the drawing board on the basic things.

So how close were you able to get on your first iteration of the helicopter design?

[This video shows] a very early demo which was done more or less just assuming that things were going to behave as they would on Earth, and that we’d be able to fly in a Martian atmosphere just spinning the rotor faster and having a very light helicopter. We were basically just trying to demonstrate that we could produce enough lift. You can see the helicopter hopping around, with someone trying to joystick it, but it turned out to be very hard to control. This was prior to doing any of the modeling that I talked about earlier. But once we started seriously focusing on the modeling and simulation, we then went on to build a prototype vehicle which had a full-size rotor that’s very close to the rotor that will be flying on Mars. One difference is that prototype had cyclic control only on the lower rotor, and later we added cyclic control on the upper rotor as well, and that decision was informed in large part by the work we did in simulation—we’d put in the kinds of disturbances that we thought we might see on Mars, and decided that we needed to have the extra control authority.

How much room do you think there is for improvement in simulation, and how could that help you in the future?

The tools that we have were definitely sufficient for doing the job that we needed to do in terms of building a helicopter that can fly on Mars. But simulation is a compute-intensive thing, and so I think there’s definitely room for higher fidelity simulation if you have the compute power to do so. For a future Mars helicopter, you could get some benefits by more closely coupling together high-fidelity aerodynamic models with larger multi-body models, and doing that in a fast way, where you can iterate quickly. There’s certainly more potential for optimizing things.

Photo: NASA/JPL-Caltech

Ingenuity preparing for flight.

Watching Ingenuity’s first flight take place will likely be much like watching the Perseverance landing—we’ll be able to follow along with the Ingenuity team while they send commands to the helicopter and receive data back, although the time delay will mean that any kind of direct control won’t be possible. If everything goes the way it’s supposed to, there will hopefully be some preliminary telemetry from Ingenuity saying so, but it sounds like we’ll likely have to wait until April 12 before we get pictures or video of the flight itself.

Because Mars doesn’t care what time it is on Earth, the flight will actually be taking place very early on April 12, with the JPL Mission Control livestream starting at 3:30 a.m. EDT (12:30 a.m. PDT). Details are here. Continue reading

Posted in Human Robots

#439036 Video Friday: Shadow Plays Jenga, and ...

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

RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
DARPA SubT Finals – September 21-23, 2021 – Louisville, KY, USA
WeRobot 2021 – September 23-25, 2021 – Coral Gables, FL, USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

The Shadow Robot team couldn't resist! Our Operator, Joanna, is using the Shadow Teleoperation System which, fun and games aside, can help those in difficult, dangerous and distant jobs.

Shadow could challenge this MIT Jenga-playing robot, but I bet they wouldn't win:

[ Shadow Robot ]

Digit is gradually stomping the Agility Robotics logo into a big grassy field fully autonomously.

[ Agility Robotics ]

This is a pretty great and very short robotic magic show.

[ Mario the Magician ]

A research team at the Georgia Institute of Technology has developed a modular solution for drone delivery of larger packages without the need for a complex fleet of drones of varying sizes. By allowing teams of small drones to collaboratively lift objects using an adaptive control algorithm, the strategy could allow a wide range of packages to be delivered using a combination of several standard-sized vehicles.

[ GA Tech ]

I've seen this done using vision before, but Flexiv's Rizon 4s can keep a ball moving along a specific trajectory using only force sensing and control.

[ Flexiv ]

Thanks Yunfan!

This combination of a 3D aerial projection system and a sensing interface can be used as an interactive and intuitive control system for things like robot arms, but in this case, it's being used to make simulated pottery. Much less messy than the traditional way of doing it.

More details on Takafumi Matsumaru's work at the Bio-Robotics & Human-Mechatronics Laboratory at Waseda University at the link below.

[ BLHM ]

U.S. Vice President Kamala Harris called astronauts Shannon Walker and Kate Rubins on the ISS, and they brought up Astrobee, at which point Shannon reaches over and rips Honey right off of her charging dock to get her on camera.

[ NASA ]

Here's a quick three minute update on Perseverance and Ingenuity from JPL.

[ Mars 2020 ]

Rigid grippers used in existing aerial manipulators require precise positioning to achieve successful grasps and transmit large contact forces that may destabilize the drone. This limits the speed during grasping and prevents “dynamic grasping,” where the drone attempts to grasp an object while moving. On the other hand, biological systems (e.g. birds) rely on compliant and soft parts to dampen contact forces and compensate for grasping inaccuracy, enabling impressive feats. This paper presents the first prototype of a soft drone—a quadrotor where traditional (i.e. rigid) landing gears are replaced with a soft tendon-actuated gripper to enable aggressive grasping.

[ MIT ]

In this video we present results from a field deployment inside the Løkken Mine underground pyrite mine in Norway. The Løkken mine was operative from 1654 to 1987 and contains narrow but long corridors, alongside vast rooms and challenging vertical stopes. In this field study we evaluated selected autonomous exploration and visual search capabilities of a subset of the aerial robots of Team CERBERUS towards the goal of complete subterranean autonomy.

[ Team CERBERUS ]

What you can do with a 1,000 FPS projector with a high speed tracking system.

[ Ishikawa Group ]

ANYbotics’ collaboration with BASF, one of the largest global chemical manufacturers, displays the efficiency, quality, and scalability of robotic inspection and data-collection capabilities in complex industrial environments.

[ ANYbotics ]

Does your robot arm need a stylish jacket?

[ Fraunhofer ]

Trossen Robotics unboxes a Unitree A1, and it's actually an unboxing where they have to figure out everything from scratch.

[ Trossen ]

Robots have learned to drive cars, assist in surgeries―and vacuum our floors. But can they navigate the unwritten rules of a busy sidewalk? Until they can, robotics experts Leila Takayama and Chris Nicholson believe, robots won’t be able to fulfill their immense potential. In this conversation, Chris and Leila explore the future of robotics and the role open source will play in it.

[ Red Hat ]

Christoph Bartneck's keynote at the 6th Joint UAE Symposium on Social Robotics, focusing on what roles robots can play during the Covid crisis and why so many social robots fail in the market.

[ HIT Lab ]

Decision-making based on arbitrary criteria is legal in some contexts, such as employment, and not in others, such as criminal sentencing. As algorithms replace human deciders, HAI-EIS fellow Kathleen Creel argues arbitrariness at scale is morally and legally problematic. In this HAI seminar, she explains how the heart of this moral issue relates to domination and a lack of sufficient opportunity for autonomy. It relates in interesting ways to the moral wrong of discrimination. She proposes technically informed solutions that can lessen the impact of algorithms at scale and so mitigate or avoid the moral harm identified.

[ Stanford HAI ]

Sawyer B. Fuller speaks on Autonomous Insect-Sized Robots at the UC Berkeley EECS Colloquium series.

Sub-gram (insect-sized) robots have enormous potential that is largely untapped. From a research perspective, their extreme size, weight, and power (SWaP) constraints also forces us to reimagine everything from how they compute their control laws to how they are fabricated. These questions are the focus of the Autonomous Insect Robotics Laboratory at the University of Washington. I will discuss potential applications for insect robots and recent advances from our group. These include the first wireless flights of a sub-gram flapping-wing robot that weighs barely more than a toothpick. I will describe efforts to expand its capabilities, including the first multimodal ground-flight locomotion, the first demonstration of steering control, and how to find chemical plume sources by integrating the smelling apparatus of a live moth. I will also describe a backpack for live beetles with a steerable camera and conceptual design of robots that could scale all the way down to the “gnat robots” first envisioned by Flynn & Brooks in the ‘80s.

[ UC Berkeley ]

Thanks Fan!

Joshua Vander Hook, Computer Scientist, NIAC Fellow, and Technical Group Supervisor at NASA JPL, presents an overview of the AI Group(s) at JPL, and recent work on single and multi-agent autonomous systems supporting space exploration, Earth science, NASA technology development, and national defense programs.

[ UMD ] Continue reading

Posted in Human Robots

#439010 Video Friday: Nanotube-Powered Insect ...

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

HRI 2021 – March 8-11, 2021 – [Online Conference]
RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
Let us know if you have suggestions for next week, and enjoy today's videos.

If you’ve ever swatted a mosquito away from your face, only to have it return again (and again and again), you know that insects can be remarkably acrobatic and resilient in flight. Those traits help them navigate the aerial world, with all of its wind gusts, obstacles, and general uncertainty. Such traits are also hard to build into flying robots, but MIT Assistant Professor Kevin Yufeng Chen has built a system that approaches insects’ agility.

Chen’s actuators can flap nearly 500 times per second, giving the drone insect-like resilience. “You can hit it when it’s flying, and it can recover,” says Chen. “It can also do aggressive maneuvers like somersaults in the air.” And it weighs in at just 0.6 grams, approximately the mass of a large bumble bee. The drone looks a bit like a tiny cassette tape with wings, though Chen is working on a new prototype shaped like a dragonfly.

[ MIT ]

National Robotics Week is April 3-11, 2021!

[ NRW ]

This is in a motion capture environment, but still, super impressive!

[ Paper ]

Thanks Fan!

Why wait for Boston Dynamics to add an arm to your Spot if you can just do it yourself?

[ ETHZ ]

This video shows the deep-sea free swimming of soft robot in the South China Sea. The soft robot was grasped by a robotic arm on ‘HAIMA’ ROV and reached the bottom of the South China Sea (depth of 3,224 m). After the releasing, the soft robot was actuated with an on-board AC voltage of 8 kV at 1 Hz and demonstrated free swimming locomotion with its flapping fins.

Um, did they bring it back?

[ Nature ]

Quadruped Yuki Mini is 12 DOF robot equipped with a Raspberry Pi that runs ROS. Also, BUNNIES!

[ Lingkang Zhang ]

Thanks Lingkang!

Deployment of drone swarms usually relies on inter-agent communication or visual markers that are mounted on the vehicles to simplify their mutual detection. The vswarm package enables decentralized vision-based control of drone swarms without relying on inter-agent communication or visual fiducial markers. The results show that the drones can safely navigate in an outdoor environment despite substantial background clutter and difficult lighting conditions.

[ Vswarm ]

A conventional adopted method for operating a waiter robot is based on the static position control, where pre-defined goal positions are marked on a map. However, this solution is not optimal in a dynamic setting, such as in a coffee shop or an outdoor catering event, because the customers often change their positions. We explore an alternative human-robot interface design where a human operator communicates the identity of the customer to the robot instead. Inspired by how [a] human communicates, we propose a framework for communicating a visual goal to the robot, through interactive two-way communications.

[ Paper ]

Thanks Poramate!

In this video, LOLA reacts to undetected ground height changes, including a drop and leg-in-hole experiment. Further tests show the robustness to vertical disturbances using a seesaw. The robot is technically blind, not using any camera-based or prior information on the terrain.

[ TUM ]

RaiSim is a cross-platform multi-body physics engine for robotics and AI. It fully supports Linux, Mac OS, and Windows.

[ RaiSim ]

Thanks Fan!

The next generation of LoCoBot is here. The LoCoBot is an ROS research rover for mapping, navigation and manipulation (optional) that enables researchers, educators and students alike to focus on high level code development instead of hardware and building out lower level code. Development on the LoCoBot is simplified with open source software, full ROS-mapping and navigation packages and modular opensource Python API that allows users to move the platform as well as (optional) manipulator in as few as 10 lines of code.

[ Trossen ]

MIT Media Lab Research Specialist Dr. Kate Darling looks at how robots are portrayed in popular film and TV shows.

Kate's book, The New Breed: What Our History with Animals Reveals about Our Future with Robots can be pre-ordered now and comes out next month.

[ Kate Darling ]

The current autonomous mobility systems for planetary exploration are wheeled rovers, limited to flat, gently-sloping terrains and agglomerate regolith. These vehicles cannot tolerate instability and operate within a low-risk envelope (i.e., low-incline driving to avoid toppling). Here, we present ‘Mars Dogs’ (MD), four-legged robotic dogs, the next evolution of extreme planetary exploration.

[ Team CoSTAR ]

In 2020, first-year PhD students at the MIT Media Lab were tasked with a special project—to reimagine the Lab and write sci-fi stories about the MIT Media Lab in the year 2050. “But, we are researchers. We don't only write fiction, we also do science! So, we did what scientists do! We used a secret time machine under the MIT dome to go to the year 2050 and see what’s going on there! Luckily, the Media Lab still exists and we met someone…really cool!” Enjoy this interview of Cyber Joe, AI Mentor for MIT Media Lab Students of 2050.

[ MIT ]

In this talk, we will give an overview of the diverse research we do at CSIRO’s Robotics and Autonomous Systems Group and delve into some specific technologies we have developed including SLAM and Legged robotics. We will also give insights into CSIRO’s participation in the current DARPA Subterranean Challenge where we are deploying a fleet of heterogeneous robots into GPS-denied unknown underground environments.

[ GRASP Seminar ]

Marco Hutter (ETH) and Hae-Won Park (KAIST) talk about “Robotics Inspired by Nature.”

[ Swiss-Korean Science Club ]

Thanks Fan!

In this keynote, Guy Hoffman Assistant Professor and the Mills Family Faculty Fellow in the Sibley School of Mechanical and Aerospace Engineering at Cornell University, discusses “The Social Uncanny of Robotic Companions.”

[ Designerly HRI ] Continue reading

Posted in Human Robots