Tag Archives: Space
#437639 Boston Dynamics’ Spot Is Helping ...
In terms of places where you absolutely want a robot to go instead of you, what remains of the utterly destroyed Chernobyl Reactor 4 should be very near the top of your list. The reactor, which suffered a catastrophic meltdown in 1986, has been covered up in almost every way possible in an effort to keep its nuclear core contained. But eventually, that nuclear material is going to have to be dealt with somehow, and in order to do that, it’s important to understand which bits of it are just really bad, and which bits are the actual worst. And this is where Spot is stepping in to help.
The big open space that Spot is walking through is right next to what’s left of Reactor 4. Within six months of the disaster, Reactor 4 was covered in a sarcophagus made of concrete and steel to try and keep all the nasty nuclear fuel from leaking out more than it already had, and it still contains “30 tons of highly contaminated dust, 16 tons of uranium and plutonium, and 200 tons of radioactive lava.” Oof. Over the next 10 years, the sarcophagus slowly deteriorated, and despite the addition of that gigantic network of steel support beams that you can see in the video, in the late 1990s it was decided to erect an enormous building over the entire mess to try and stabilize it for as long as possible.
Reactor 4 is now snugly inside the massive New Safe Confinement (NSC) structure, and the idea is that eventually, the structure will allow for the safe disassembly of what’s left of the reactor, although nobody is quite sure how to do that. This is all just to say that the area inside of the containment structure offers a lot of good opportunities for robots to take over from humans.
This particular Spot is owned by the U.K. Atomic Energy Authority, and was packed off to Russia with the assistance of the Robotics and Artificial Intelligence in Nuclear (RAIN) initiative and the National Centre for Nuclear Robotics. Dr. Dave Megson-Smith, who is a researcher at the University of Bristol, in the U.K., and part of the Hot Robotics Facility at the National Nuclear User Facility, was one of the scientists lucky enough to accompany Spot on its adventure. Megson-Smith specializes in sensor development, and he equipped Spot with a collimated radiation sensor in addition to its mapping payload. “We actually built a map of the radiation coming out of the front wall of Chernobyl power plant as we were in there with it,” Megson-Smith told us, and was able to share this picture, which shows a map of gamma photon count rate:
Image: University of Bristol
Researchers equipped Spot with a collimated radiation sensor and use one of the data readings (gamma photon count rate) to create a map of the radiation coming out of the front wall of the Chernobyl power plant.
So what’s the reason you’d want to use a very expensive legged robot to wander around what looks like a very flat and robot friendly floor? As it turns out, the floor is very dusty in there, and a priority inside the NSC is to keep dust down as much as possible, since the dust is radioactive and gets on everything and is consequently the easiest way for radioactivity to escape the NSC. “You want to minimize picking up material, so we consider the total contact surface area,” says Megson-Smith. “If you use a legged system rather than a wheeled or tracked system, you have a much smaller footprint and you disturb the environment a lot less.” While it’s nice that Spot is nimble and can climb stairs and stuff, tracked vehicles can do that as well, so in this case, the primary driving factor of choosing a robot to work inside Chernobyl is minimizing those contact points.
Right now, routine weekly measurements in contaminated spaces at Chernobyl are done by humans, which puts those humans at risk. Spot, or a robot like it, could potentially take over from those humans, as a sort of “automated safety checker”
Right now, routine weekly measurements in contaminated spaces at Chernobyl are done by humans, which puts those humans at risk. Spot, or a robot like it, could potentially take over from those humans, as a sort of “automated safety checker” able to work in medium level contaminated environments.” As far as more dangerous areas go, there’s a lot of uncertainty about what Spot is actually capable of, according to Megson-Smith. “What you think the problems are, and what the industry thinks the problems are, are subtly different things.
We were thinking that we’d have to make robots incredibly radiation proof to go into these contaminated environments, but they said, “can you just give us a system that we can send into places where humans already can go, but where we just don’t want to send humans.” Making robots incredibly radiation proof is challenging, and without extensive testing and ruggedizing, failures can be frequent, as many robots discovered at Fukushima. Indeed, Megson-Smith that in Fukushima there’s a particular section that’s known as a “robot graveyard” where robots just go to die, and they’ve had to up their standards again and again to keep the robots from failing. “So the thing they’re worried about with Spot is, what is its tolerance? What components will fail, and what can we do to harden it?” he says. “We’re approaching Boston Dynamics at the moment to see if they’ll work with us to address some of those questions.
There’s been a small amount of testing of how robots fair under harsh radiation, Megson-Smith told us, including (relatively recently) a KUKA LBR800 arm, which “stopped operating after a large radiation dose of 164.55(±1.09) Gy to its end effector, and the component causing the failure was an optical encoder.” And in case you’re wondering how much radiation that is, a 1 to 2 Gy dose to the entire body gets you acute radiation sickness and possibly death, while 8 Gy is usually just straight-up death. The goal here is not to kill robots (I mean, it sort of is), but as Megson-Smith says, “if we can work out what the weak points are in a robotic system, can we address those, can we redesign those, or at least understand when they might start to fail?” Now all he has to do is convince Boston Dynamics to send them a Spot that they can zap until it keels over.
The goal for Spot in the short term is fully autonomous radiation mapping, which seems very possible. It’ll also get tested with a wider range of sensor packages, and (happily for the robot) this will all take place safely back at home in the U.K. As far as Chernobyl is concerned, robots will likely have a substantial role to play in the near future. “Ultimately, Chernobyl has to be taken apart and decommissioned. That’s the long-term plan for the facility. To do that, you first need to understand everything, which is where we come in with our sensor systems and robotic platforms,” Megson-Smith tells us. “Since there are entire swathes of the Chernobyl nuclear plant where people can’t go in, we’d need robots like Spot to do those environmental characterizations.” Continue reading →
Posted in Human Robots
#437628 Video Friday: An In-Depth Look at Mesmer ...
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!):
AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Online]
IROS 2020 – October 25-29, 2020 – [Online]
ROS World 2020 – November 12, 2020 – [Online]
CYBATHLON 2020 – November 13-14, 2020 – [Online]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.
Bear Robotics, a robotics and artificial intelligence company, and SoftBank Robotics Group, a leading robotics manufacturer and solutions provider, have collaborated to bring a new robot named Servi to the food service and hospitality field.
[ Bear Robotics ]
A literal in-depth look at Engineered Arts’ Mesmer android.
[ Engineered Arts ]
Is your robot running ROS? Is it connected to the Internet? Are you actually in control of it right now? Are you sure?
I appreciate how the researchers admitted to finding two of their own robots as part of the scan, a Baxter and a drone.
[ Brown ]
Smile Robotics describes this as “(possibly) world’s first full-autonomous clear-up-the-table robot.”
We’re not qualified to make a judgement on the world firstness, but personally I hate clearing tables, so this robot has my vote.
Smile Robotics founder and CEO Takashi Ogura, along with chief engineer Mitsutaka Kabasawa and engineer Kazuya Kobayashi, are former Google roboticists. Ogura also worked at SCHAFT. Smile says its robot uses ROS and is controlled by a framework written mainly in Rust, adding: “We are hiring Rustacean Roboticists!”
[ Smile Robotics ]
We’re not entirely sure why, but Panasonic has released plans for an Internet of Things system for hamsters.
We devised a recipe for a “small animal healthcare device” that can measure the weight and activity of small animals, the temperature and humidity of the breeding environment, and manage their health. This healthcare device visualizes the health status and breeding environment of small animals and manages their health to promote early detection of diseases. While imagining the scene where a healthcare device is actually used for an important small animal that we treat with affection, we hope to help overcome the current difficult situation through manufacturing.
[ Panasonic ] via [ RobotStart ]
Researchers at Yale have developed a robotic fabric, a breakthrough that could lead to such innovations as adaptive clothing, self-deploying shelters, or lightweight shape-changing machinery.
The researchers focused on processing functional materials into fiber-form so they could be integrated into fabrics while retaining its advantageous properties. For example, they made variable stiffness fibers out of an epoxy embedded with particles of Field’s metal, an alloy that liquifies at relatively low temperatures. When cool, the particles are solid metal and make the material stiffer; when warm, the particles melt into liquid and make the material softer.
[ Yale ]
In collaboration with Armasuisse and SBB, RSL demonstrated the use of a teleoperated Menzi Muck M545 to clean up a rock slide in Central Switzerland. The machine can be operated from a teloperation platform with visual and motion feedback. The walking excavator features an active chassis that can adapt to uneven terrain.
[ ETHZ RSL ]
An international team of JKU researchers is continuing to develop their vision for robots made out of soft materials. A new article in the journal “Communications Materials” demonstrates just how these kinds of soft machines react using weak magnetic fields to move very quickly. A triangle-shaped robot can roll itself in air at high speed and walk forward when exposed to an alternating in-plane square wave magnetic field (3.5 mT, 1.5 Hz). The diameter of the robot is 18 mm with a thickness of 80 µm. A six-arm robot can grab, transport, and release non-magnetic objects such as a polyurethane foam cube controlled by a permanent magnet.
Okay but tell me more about that cute sheep.
[ JKU ]
Interbotix has this “research level robotic crawler,” which both looks mean and runs ROS, a dangerous combination.
And here’s how it all came together:
[ Interbotix ]
I guess if you call them “loitering missile systems” rather than “drones that blow things up” people are less likely to get upset?
[ AeroVironment ]
In this video, we show a planner for a master dual-arm robot to manipulate tethered tools with an assistant dual-arm robot’s help. The assistant robot provides assistance to the master robot by manipulating the tool cable and avoiding collisions. The provided assistance allows the master robot to perform tool placements on the robot workspace table to regrasp the tool, which would typically fail since the tool cable tension may change the tool positions. It also allows the master robot to perform tool handovers, which would normally cause entanglements or collisions with the cable and the environment without the assistance.
[ Harada Lab ]
This video shows a flexible and robust robotic system for autonomous drawing on 3D surfaces. The system takes 2D drawing strokes and a 3D target surface (mesh or point clouds) as input. It maps the 2D strokes onto the 3D surface and generates a robot motion to draw the mapped strokes using visual recognition, grasp pose reasoning, and motion planning.
[ Harada Lab ]
Weekly mobility test. This time the Warthog takes on a fallen tree. Will it cross it? The answer is in the video!
And the answer is: kinda?
[ NORLAB ]
One of the advantages of walking machines is their ability to apply forces in all directions and of various magnitudes to the environment. Many of the multi-legged robots are equipped with point contact feet as these simplify the design and control of the robot. The iStruct project focuses on the development of a foot that allows extensive contact with the environment.
[ DFKI ]
An urgent medical transport was simulated in NASA’s second Systems Integration and Operationalization (SIO) demonstration Sept. 28 with partner Bell Textron Inc. Bell used the remotely-piloted APT 70 to conduct a flight representing an urgent medical transport mission. It is envisioned in the future that an operational APT 70 could provide rapid medical transport for blood, organs, and perishable medical supplies (payload up to 70 pounds). The APT 70 is estimated to move three times as fast as ground transportation.
Always a little suspicious when the video just shows the drone flying, and sitting on the ground, but not that tricky transition between those two states.
[ NASA ]
A Lockheed Martin Robotics Seminar on “Socially Assistive Mobile Robots,” by Yi Guo from Stevens Institute of Technology.
The use of autonomous mobile robots in human environments is on the rise. Assistive robots have been seen in real-world environments, such as robot guides in airports, robot polices in public parks, and patrolling robots in supermarkets. In this talk, I will first present current research activities conducted in the Robotics and Automation Laboratory at Stevens. I’ll then focus on robot-assisted pedestrian regulation, where pedestrian flows are regulated and optimized through passive human-robot interaction.
[ UMD ]
This week’s CMU RI Seminar is by CMU’s Zachary Manchester, on “The World’s Tiniest Space Program.”
The aerospace industry has experienced a dramatic shift over the last decade: Flying a spacecraft has gone from something only national governments and large defense contractors could afford to something a small startup can accomplish on a shoestring budget. A virtuous cycle has developed where lower costs have led to more launches and the growth of new markets for space-based data. However, many barriers remain. This talk will focus on driving these trends to their ultimate limit by harnessing advances in electronics, planning, and control to build spacecraft that cost less than a new smartphone and can be deployed in large numbers.
[ CMU RI ] Continue reading →
Posted in Human Robots
#437624 AI-Powered Drone Learns Extreme ...
Quadrotors are among the most agile and dynamic machines ever created. In the hands of a skilled human pilot, they can do some astonishing series of maneuvers. And while autonomous flying robots have been getting better at flying dynamically in real-world environments, they still haven’t demonstrated the same level of agility of manually piloted ones.
Now researchers from the Robotics and Perception Group at the University of Zurich and ETH Zurich, in collaboration with Intel, have developed a neural network training method that “enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation.” Extreme.
There are two notable things here: First, the quadrotor can do these extreme acrobatics outdoors without any kind of external camera or motion-tracking system to help it out (all sensing and computing is onboard). Second, all of the AI training is done in simulation, without the need for an additional simulation-to-real-world (what researchers call “sim-to-real”) transfer step. Usually, a sim-to-real transfer step means putting your quadrotor into one of those aforementioned external tracking systems, so that it doesn’t completely bork itself while trying to reconcile the differences between the simulated world and the real world, where, as the researchers wrote in a paper describing their system, “even tiny mistakes can result in catastrophic outcomes.”
To enable “zero-shot” sim-to-real transfer, the neural net training in simulation uses an expert controller that knows exactly what’s going on to teach a “student controller” that has much less perfect knowledge. That is, the simulated sensory input that the student ends up using as it learns to follow the expert has been abstracted to present the kind of imperfect, imprecise data it’s going to encounter in the real world. This can involve things like abstracting away the image part of the simulation until you’d have no way of telling the difference between abstracted simulation and abstracted reality, which is what allows the system to make that sim-to-real leap.
The simulation environment that the researchers used was Gazebo, slightly modified to better simulate quadrotor physics. Meanwhile, over in reality, a custom 1.5-kilogram quadrotor with a 4:1 thrust to weight ratio performed the physical experiments, using only a Nvidia Jetson TX2 computing board and an Intel RealSense T265, a dual fisheye camera module optimized for V-SLAM. To challenge the learning system, it was trained to perform three acrobatic maneuvers plus a combo of all of them:
Image: University of Zurich/ETH Zurich/Intel
Reference trajectories for acrobatic maneuvers. Top row, from left: Power Loop, Barrel Roll, and Matty Flip. Bottom row: Combo.
All of these maneuvers require high accelerations of up to 3 g’s and careful control, and the Matty Flip is particularly challenging, at least for humans, because the whole thing is done while the drone is flying backwards. Still, after just a few hours of training in simulation, the drone was totally real-world competent at these tricks, and could even extrapolate a little bit to perform maneuvers that it was not explicitly trained on, like doing multiple loops in a row. Where humans still have the advantage over drones is (as you might expect since we’re talking about robots) is quickly reacting to novel or unexpected situations. And when you’re doing this sort of thing outdoors, novel and unexpected situations are everywhere, from a gust of wind to a jealous bird.
For more details, we spoke with Antonio Loquercio from the University of Zurich’s Robotics and Perception Group.
IEEE Spectrum: Can you explain how the abstraction layer interfaces with the simulated sensors to enable effective sim-to-real transfer?
Antonio Loquercio: The abstraction layer applies a specific function to the raw sensor information. Exactly the same function is applied to the real and simulated sensors. The result of the function, which is “abstracted sensor measurements,” makes simulated and real observation of the same scene similar. For example, suppose we have a sequence of simulated and real images. We can very easily tell apart the real from the simulated ones given the difference in rendering. But if we apply the abstraction function of “feature tracks,” which are point correspondences in time, it becomes very difficult to tell which are the simulated and real feature tracks, since point correspondences are independent of the rendering. This applies for humans as well as for neural networks: Training policies on raw images gives low sim-to-real transfer (since images are too different between domains), while training on the abstracted images has high transfer abilities.
How useful is visual input from a camera like the Intel RealSense T265 for state estimation during such aggressive maneuvers? Would using an event camera substantially improve state estimation?
Our end-to-end controller does not require a state estimation module. It shares however some components with traditional state estimation pipelines, specifically the feature extractor and the inertial measurement unit (IMU) pre-processing and integration function. The input of the neural networks are feature tracks and integrated IMU measurements. When looking at images with low features (for example when the camera points to the sky), the neural net will mainly rely on IMU. When more features are available, the network uses to correct the accumulated drift from IMU. Overall, we noticed that for very short maneuvers IMU measurements were sufficient for the task. However, for longer ones, visual information was necessary to successfully address the IMU drift and complete the maneuver. Indeed, visual information reduces the odds of a crash by up to 30 percent in the longest maneuvers. We definitely think that event camera can improve even more the current approach since they could provide valuable visual information during high speed.
“The Matty Flip is probably one of the maneuvers that our approach can do very well … It is super challenging for humans, since they don’t see where they’re going and have problems in estimating their speed. For our approach the maneuver is no problem at all, since we can estimate forward velocities as well as backward velocities.”
—Antonio Loquercio, University of Zurich
You describe being able to train on “maneuvers that stretch the abilities of even expert human pilots.” What are some examples of acrobatics that your drones might be able to do that most human pilots would not be capable of?
The Matty Flip is probably one of the maneuvers that our approach can do very well, but human pilots find very challenging. It basically entails doing a high speed power loop by always looking backward. It is super challenging for humans, since they don’t see where they’re going and have problems in estimating their speed. For our approach the maneuver is no problem at all, since we can estimate forward velocities as well as backward velocities.
What are the limits to the performance of this system?
At the moment the main limitation is the maneuver duration. We never trained a controller that could perform maneuvers longer than 20 seconds. In the future, we plan to address this limitation and train general controllers which can fly in that agile way for significantly longer with relatively small drift. In this way, we could start being competitive against human pilots in drone racing competitions.
Can you talk about how the techniques developed here could be applied beyond drone acrobatics?
The current approach allows us to do acrobatics and agile flight in free space. We are now working to perform agile flight in cluttered environments, which requires a higher degree of understanding of the surrounding with respect to this project. Drone acrobatics is of course only an example application. We selected it because it makes a stress test of the controller performance. However, several other applications which require fast and agile flight can benefit from our approach. Examples are delivery (we want our Amazon packets always faster, don’t we?), search and rescue, or inspection. Going faster allows us to cover more space in less time, saving battery costs. Indeed, agile flight has very similar battery consumption of slow hovering for an autonomous drone.
“Deep Drone Acrobatics,” by Elia Kaufmann, Antonio Loquercio, René Ranftl, Matthias Müller, Vladlen Koltun, and Davide Scaramuzza from the Robotics and Perception Group at the University of Zurich and ETH Zurich, and Intel’s Intelligent Systems Lab, was presented at RSS 2020. Continue reading →
Posted in Human Robots
#437614 Video Friday: Poimo Is a Portable ...
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!):
IROS 2020 – October 25-29, 2020 – [Online]
ROS World 2020 – November 12, 2020 – [Online]
CYBATHLON 2020 – November 13-14, 2020 – [Online]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today's videos.
Engineers at the University of California San Diego have built a squid-like robot that can swim untethered, propelling itself by generating jets of water. The robot carries its own power source inside its body. It can also carry a sensor, such as a camera, for underwater exploration.
[ UCSD ]
Thanks Ioana!
Shark Robotics, French and European leader in Unmanned Ground Vehicles, is announcing today a disinfection add-on for Boston Dynamics Spot robot, designed to fight the COVID-19 pandemic. The Spot robot with Shark’s purpose-built disinfection payload can decontaminate up to 2,000 m2 in 15 minutes, in any space that needs to be sanitized – such as hospitals, metro stations, offices, warehouses or facilities.
[ Shark Robotics ]
Here’s an update on the Poimo portable inflatable mobility project we wrote about a little while ago; while not strictly robotics, it seems like it holds some promise for rapidly developing different soft structures that robotics might find useful.
[ University of Tokyo ]
Thanks Ryuma!
Pretty cool that you can do useful force feedback teleop while video chatting through a “regular broadband Internet connection.” Although, what “regular” means to you is a bit subjective, right?
[ HEBI Robotics ]
Thanks Dave!
While NASA's Mars rover Perseverance travels through space toward the Red Planet, its nearly identical rover twin is hard at work on Earth. The vehicle system test bed (VSTB) rover named OPTIMISM is a full-scale engineering version of the Mars-bound rover. It is used to test hardware and software before the commands are sent up to the Perseverance rover.
[ NASA ]
Jacquard takes ordinary, familiar objects and enhances them with new digital abilities and experiences, while remaining true to their original purpose — like being your favorite jacket, backpack or a pair of shoes that you love to wear.
Our ambition is simple: to make life easier. By staying connected to your digital world, your things can do so much more. Skip a song by brushing your sleeve. Take a picture by tapping on a shoulder strap. Get reminded about the phone you left behind with a blink of light or a haptic buzz on your cuff.
[ Google ATAP ]
Should you attend the IROS 2020 workshop on “Planetary Exploration Robots: Challenges and Opportunities”? Of course you should!
[ Workshop ]
Kuka makes a lot of these videos where I can’t help but think that if they put as much effort into programming the robot as they did into producing the video, the result would be much more impressive.
[ Kuka ]
The Colorado School of Mines is one of the first customers to buy a Spot robot from Boston Dynamics to help with robotics research. Watch as scientists take Spot into the school's mine for the first time.
[ HCR ] via [ CNET ]
A very interesting soft(ish) actuator from Ayato Kanada at Kyushu University's Control Engineering Lab.
A flexible ultrasonic motor (FUSM), which generates linear motion as a novel soft actuator. This motor consists of a single metal cube stator with a hole and an elastic elongated coil spring inserted into the hole. When voltages are applied to piezoelectric plates on the stator, the coil spring moves back and forward as a linear slider. In the FUSM that uses the friction drive as the principle, the most important parameter for optimizing its output is the preload between the stator and slider. The coil spring has a slightly larger diameter than the stator hole and generates the preload by expanding in a radial direction. The coil springs act not only as a flexible slider but also as a resistive positional sensor. Changes in the resistance between the stator and the coil spring end are converted to a voltage and used for position detection.
[ Control Engineering Lab ]
Thanks Ayato!
We show how to use the limbs of a quadruped robot to identify fine-grained soil, representative for Martian regolith.
[ Paper ] via [ ANYmal Research ]
PR2 is serving breakfast and cleaning up afterwards. It’s slow, but all you have to do is eat and leave.
That poor PR2 is a little more naked than it's probably comfortable with.
[ EASE ]
NVIDIA researchers present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped robot (the Unitree Laikago).
[ NVIDIA ]
What's interesting about this assembly task is that the robot is using its arm only for positioning, and doing the actual assembly with just fingers.
[ RC2L ]
In this electronics assembly application, Kawasaki's cobot duAro2 uses a tool changing station to tackle a multitude of tasks and assemble different CPU models.
Okay but can it apply thermal paste to a CPU in the right way? Personally, I find that impossible.
[ Kawasaki ]
You only need to watch this video long enough to appreciate the concept of putting a robot on a robot.
[ Impress ]
In this lecture, we’ll hear from the man behind one of the biggest robotics companies in the world, Boston Dynamics, whose robotic dog, Spot, has been used to encourage social distancing in Singapore and is now getting ready for FDA approval to be able to measure patients’ vital signs in hospitals.
[ Alan Turing Institute ]
Greg Kahn from UC Berkeley wrote in to share his recent dissertation talk on “Mobile Robot Learning.”
In order to create mobile robots that can autonomously navigate real-world environments, we need generalizable perception and control systems that can reason about the outcomes of navigational decisions. Learning-based methods, in which the robot learns to navigate by observing the outcomes of navigational decisions in the real world, offer considerable promise for obtaining these intelligent navigation systems. However, there are many challenges impeding mobile robots from autonomously learning to act in the real-world, in particular (1) sample-efficiency–how to learn using a limited amount of data? (2) supervision–how to tell the robot what to do? and (3) safety–how to ensure the robot and environment are not damaged or destroyed during learning? In this talk, I will present deep reinforcement learning methods for addressing these real world mobile robot learning challenges and show results which enable ground and aerial robots to navigate in complex indoor and outdoor environments.
[ UC Berkeley ]
Thanks Greg!
Leila Takayama from UC Santa Cruz (and previously Google X and Willow Garage) gives a talk entitled “Toward a more human-centered future of robotics.”
Robots are no longer only in outer space, in factory cages, or in our imaginations. We interact with robotic agents when withdrawing cash from bank ATMs, driving cars with adaptive cruise control, and tuning our smart home thermostats. In the moment of those interactions with robotic agents, we behave in ways that do not necessarily align with the rational belief that robots are just plain machines. Through a combination of controlled experiments and field studies, we use theories and concepts from the social sciences to explore ways that human and robotic agents come together, including how people interact with personal robots and how people interact through telepresence robots. Together, we will explore topics and raise questions about the psychology of human-robot interaction and how we could invent a future of a more human-centered robotics that we actually want to live in.
[ Leila Takayama ]
Roboticist and stand-up comedian Naomi Fitter from Oregon State University gives a talk on “Everything I Know about Telepresence.”
Telepresence robots hold promise to connect people by providing videoconferencing and navigation abilities in far-away environments. At the same time, the impacts of current commercial telepresence robots are not well understood, and circumstances of robot use including internet connection stability, odd personalizations, and interpersonal relationship between a robot operator and people co-located with the robot can overshadow the benefit of the robot itself. And although the idea of telepresence robots has been around for over two decades, available nonverbal expressive abilities through telepresence robots are limited, and suitable operator user interfaces for the robot (for example, controls that allow for the operator to hold a conversation and move the robot simultaneously) remain elusive. So where should we be using telepresence robots? Are there any pitfalls to watch out for? What do we know about potential robot expressivity and user interfaces? This talk will cover my attempts to address these questions and ways in which the robotics research community can build off of this work
[ Talking Robotics ] Continue reading →
Posted in Human Robots
#437600 Brain-Inspired Robot Controller Uses ...
Robots operating in the real world are starting to find themselves constrained by the amount of computing power they have available. Computers are certainly getting faster and more efficient, but they’re not keeping up with the potential of robotic systems, which have access to better sensors and more data, which in turn makes decision making more complex. A relatively new kind of computing device called a memristor could potentially help robotics smash through this barrier, through a combination of lower complexity, lower cost, and higher speed.
In a paper published today in Science Robotics, a team of researchers from the University of Southern California in Los Angeles and the Air Force Research Laboratory in Rome, N.Y., demonstrate a simple self-balancing robot that uses memristors to form a highly effective analog control system, inspired by the functional structure of the human brain.
First, we should go over just what the heck a memristor is. As the name suggests, it’s a type of memory that is resistance-based. That is, the resistance of a memristor can be programmed, and the memristor remembers that resistance even after it’s powered off (the resistance depends on the magnitude of the voltage applied to the memristor’s two terminals and the length of time that voltage has been applied). Memristors are potentially the ideal hybrid between RAM and flash memory, offering high speed, high density, non-volatile storage. So that’s cool, but what we’re most interested in as far as robot control systems go is that memristors store resistance, making them analog devices rather than digital ones.
By adding a memristor to an analog circuit with inputs from a gyroscope and an accelerometer, the researchers created a completely analog Kalman filter, which coupled to a second memristor functioned as a PD controller.
Nowadays, the word “analog” sounds like a bad thing, but robots are stuck in an analog world, and any physical interactions they have with the world (mediated through sensors) are fundamentally analog in nature. The challenge is that an analog signal is often “messy”—full of noise and non-linearities—and as such, the usual approach now is to get it converted to a digital signal and then processed to get anything useful out of it. This is fine, but it’s also not particularly fast or efficient. Where memristors come in is that they’re inherently analog, and in addition to storing data, they can also act as tiny analog computers, which is pretty wild.
By adding a memristor to an analog circuit with inputs from a gyroscope and an accelerometer, the researchers, led by Wei Wu, an associate professor of electrical engineering at USC, created a completely analog and completely physical Kalman filter to remove noise from the sensor signal. In addition, they used a second memristor can be used to turn that sensor data into a proportional-derivative (PD) controller. Next they put those two components together to build an analogy system that can do a bunch of the work required to keep an inverted pendulum robot upright far more efficiently than a traditional system. The difference in performance is readily apparent:
The shaking you see in the traditionally-controlled robot on the bottom comes from the non-linearity of the dynamic system, which changes faster than the on-board controller can keep up with. The memristors substantially reduce the cycle time, so the robot can balance much more smoothly. Specifically, cycle time is reduced from 3,034 microseconds to just 6 microseconds.
Of course, there’s more going on here, like motor drivers and a digital computer to talk to them, so this robot is really a hybrid system. But guess what? As the researchers point out, so are we!
The human brain consists of the cerebrum, the cerebellum, and the brainstem. The cerebrum is a major part of the brain in charge of vision, hearing, and thinking, whereas the cerebellum plays an important role in motion control. Through this cooperation of the cerebrum and the cerebellum, the human brain can conduct multiple tasks simultaneously with extremely low power consumption. Inspired by this, we developed a hybrid analog-digital computation platform, in which the digital component runs the high-level algorithm, whereas the analog component is responsible for sensor fusion and motion control.
By offloading a bunch of computation onto the memristors, the higher brain functions of the robot have more breathing room. Overall, you reduce power, space, and cost, while substantially improving performance. This has only become possible relatively recently due to memristor advances and availability, and the researchers expect that memristor-based hybrid computing will soon be able to “improve the robustness and the performance of mobile robotic systems with higher” degrees of freedom.
“A memristor-based hybrid analog-digital computing platform for mobile robotics,” by Buyun Chen, Hao Yang, Boxiang Song, Deming Meng, Xiaodong Yan, Yuanrui Li, Yunxiang Wang, Pan Hu, Tse-Hsien Ou, Mark Barnell, Qing Wu, Han Wang, and Wei Wu, from USC Viterbi and AFRL, was published in Science Robotics. Continue reading →
Posted in Human Robots