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#437643 Video Friday: Matternet Launches Urban ...
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 25-25, 2020 – [Online]
Bay Area Robotics Symposium – November 20, 2020 – [Online]
ACRA 2020 – December 8-10, 2020 – [Online]
Let us know if you have suggestions for next week, and enjoy today's videos.
Sixteen teams chose their roster of virtual robots and sensor payloads, some based on real-life subterranean robots, and submitted autonomy and mapping algorithms that SubT Challenge officials then tested across eight cave courses in the cloud-based SubT Simulator. Their robots traversed the cave environments autonomously, without any input or adjustments from human operators. The Cave Circuit Virtual Competition teams earned points by correctly finding, identifying, and localizing up to 20 artifacts hidden in the cave courses within five-meter accuracy.
[ SubT ]
This year, the KUKA Innovation Award’s international jury of experts received a total of more than 40 ideas. The five finalist teams had time until November to implement their ideas. A KUKA LBR Med lightweight robot – the first robotic component to be certified for integration into a medical device – has been made available to them for this purpose. Beyond this, the teams have received a training for the hardware and coaching from KUKA experts throughout the competition. At virtual.MEDICA from 16-19.11.2020, the finalists presented their concepts to an international audience of experts and to the Innovation Award jury.
The winner of the KUKA Innovation Award 2020, worth 20,000 euros, is Team HIFUSK from the Scuola Superiore Sant'Anna in Italy.
[ KUKA Innovation Award ]
Like everything else the in-person Cybathlon event was cancelled, but the competition itself took place, just a little more distributed than it would have been otherwise.
[ Cybathlon ]
Matternet, developer of the world's leading urban drone logistics platform, today announced the launch of operations at Labor Berlin Charité Vivantes in Germany. The program kicked-off November 17, 2020 with permanent operations expected to take flight next year, creating the first urban BVLOS [Beyond Visual Line of Sight] medical drone delivery network in the European Union. The drone network expects to significantly improve the timeliness and efficiency of Labor Berlin’s diagnostics services by providing an option to avoid roadway delays, which will improve patient experience with potentially life-saving benefits and lower costs.
Routine BVLOS over an urban area? Impressive.
[ Matternet ]
Robots playing diabolo!
Thanks Thilo!
[ OMRON Sinic X]
Anki's tech has been repackaged into this robot that serves butter:
[ Butter Robot ]
Berkshire Grey just announced our Picking With Purpose Program in which we’ve partnered our robotic automation solutions with food rescue organizations City Harvest and The Greater Boston Food Bank to pick, pack, and distribute food to families in need in time for Thanksgiving. Berkshire Grey donated about 40,000 pounds of food, used one of our robotic automation systems to pick and pack that food into meal boxes for families in need, and our team members volunteered to run the system. City Harvest and The Greater Boston Food Bank are distributing the 4,000 meal boxes we produced. This is just the beginning. We are building a sponsorship program to make Picking With Purpose an ongoing initiative.
[ Berkshire Grey ]
Thanks Peter!
We posted a video previously of Cassie learning to skip, but here's a much more detailed look (accompanying an ICRA submission) that includes some very impressive stair descending.
[ DRL ]
From garage inventors to university students and entrepreneurs, NASA is looking for ideas on how to excavate the Moon’s icy regolith, or dirt, and deliver it to a hypothetical processing plant at the lunar South Pole. The NASA Break the Ice Lunar Challenge, a NASA Centennial Challenge, is now open for registration. The competition will take place over two phases and will reward new ideas and approaches for a system architecture capable of excavating and moving icy regolith and water on the lunar surface.
[ NASA ]
Adaptation to various scene configurations and object properties, stability and dexterity in robotic grasping manipulation is far from explored. This work presents an origami-based shape morphing fingertip design to actively tackle the grasping stability and dexterity problems. The proposed fingertip utilizes origami as its skeleton providing degrees of freedom at desired positions and motor-driven four-bar-linkages as its transmission components to achieve a compact size of the fingertip.
[ Paper ]
“If Roboy crashes… you die.”
[ Roboy ]
Traditionally lunar landers, as well as other large space exploration vehicles, are powered by solar arrays or small nuclear reactors. Rovers and small robots, however, are not big enough to carry their own dedicated power supplies and must be tethered to their larger counterparts via electrical cables. Tethering severely restricts mobility, and cables are prone to failure due to lunar dust (regolith) interfering with electrical contact points. Additionally, as robots become smaller and more complex, they are fitted with additional sensors that require more power, further exacerbating the problem. Lastly, solar arrays are not viable for charging during the lunar night. WiBotic is developing rapid charging systems and energy monitoring base stations for lunar robots, including the CubeRover – a shoebox-sized robot designed by Astrobotic – that will operate autonomously and charge wirelessly on the Moon.
[ WiBotic ]
Watching pick and place robots is my therapy.
[ Soft Robotics ]
It's really, really hard to beat liquid fuel for energy storage, as Quaternium demonstrates with their hybrid drone.
[ Quaternium ]
Thanks Gregorio!
State-of-the-art quadrotor simulators have a rigid and highly-specialized structure: either are they really fast, physically accurate, or photo-realistic. In this work, we propose a novel quadrotor simulator: Flightmare.
[ Flightmare ]
Drones that chuck fire-fighting balls into burning buildings, sure!
[ LARICS ]
If you missed ROS World, that's okay, because all of the talks are now online. Here's the opening keynote from Vivian Chu and Diligent robotics, along with a couple fun lightning talks.
[ ROS World 2020 ]
This week's CMU RI Seminar is by Chelsea Finn from Stanford University, on Data Scalability for Robot Learning.
Recent progress in robot learning has demonstrated how robots can acquire complex manipulation skills from perceptual inputs through trial and error, particularly with the use of deep neural networks. Despite these successes, the generalization and versatility of robots across environment conditions, tasks, and objects remains a major challenge. And, unfortunately, our existing algorithms and training set-ups are not prepared to tackle such challenges, which demand large and diverse sets of tasks and experiences. In this talk, I will discuss two central challenges that pertain to data scalability: first, acquiring large datasets of diverse and useful interactions with the world, and second, developing algorithms that can learn from such datasets. Then, I will describe multiple approaches that we might take to rethink our algorithms and data pipelines to serve these goals. This will include algorithms that allow a real robot to explore its environment in a targeted manner with minimal supervision, approaches that can perform robot reinforcement learning with videos of human trial-and-error experience, and visual model-based RL approaches that are not bottlenecked by their capacity to model everything about the world.
[ CMU RI ] Continue reading
Posted in Human Robots
#437337 6G Will Be 100 Times Faster Than ...
Though 5G—a next-generation speed upgrade to wireless networks—is scarcely up and running (and still nonexistent in many places) researchers are already working on what comes next. It lacks an official name, but they’re calling it 6G for the sake of simplicity (and hey, it’s tradition). 6G promises to be up to 100 times faster than 5G—fast enough to download 142 hours of Netflix in a second—but researchers are still trying to figure out exactly how to make such ultra-speedy connections happen.
A new chip, described in a paper in Nature Photonics by a team from Osaka University and Nanyang Technological University in Singapore, may give us a glimpse of our 6G future. The team was able to transmit data at a rate of 11 gigabits per second, topping 5G’s theoretical maximum speed of 10 gigabits per second and fast enough to stream 4K high-def video in real time. They believe the technology has room to grow, and with more development, might hit those blistering 6G speeds.
NTU final year PhD student Abhishek Kumar, Assoc Prof Ranjan Singh and postdoc Dr Yihao Yang. Dr Singh is holding the photonic topological insulator chip made from silicon, which can transmit terahertz waves at ultrahigh speeds. Credit: NTU Singapore
But first, some details about 5G and its predecessors so we can differentiate them from 6G.
Electromagnetic waves are characterized by a wavelength and a frequency; the wavelength is the distance a cycle of the wave covers (peak to peak or trough to trough, for example), and the frequency is the number of waves that pass a given point in one second. Cellphones use miniature radios to pick up electromagnetic signals and convert those signals into the sights and sounds on your phone.
4G wireless networks run on millimeter waves on the low- and mid-band spectrum, defined as a frequency of a little less (low-band) and a little more (mid-band) than one gigahertz (or one billion cycles per second). 5G kicked that up several notches by adding even higher frequency millimeter waves of up to 300 gigahertz, or 300 billion cycles per second. Data transmitted at those higher frequencies tends to be information-dense—like video—because they’re much faster.
The 6G chip kicks 5G up several more notches. It can transmit waves at more than three times the frequency of 5G: one terahertz, or a trillion cycles per second. The team says this yields a data rate of 11 gigabits per second. While that’s faster than the fastest 5G will get, it’s only the beginning for 6G. One wireless communications expert even estimates 6G networks could handle rates up to 8,000 gigabits per second; they’ll also have much lower latency and higher bandwidth than 5G.
Terahertz waves fall between infrared waves and microwaves on the electromagnetic spectrum. Generating and transmitting them is difficult and expensive, requiring special lasers, and even then the frequency range is limited. The team used a new material to transmit terahertz waves, called photonic topological insulators (PTIs). PTIs can conduct light waves on their surface and edges rather than having them run through the material, and allow light to be redirected around corners without disturbing its flow.
The chip is made completely of silicon and has rows of triangular holes. The team’s research showed the chip was able to transmit terahertz waves error-free.
Nanyang Technological University associate professor Ranjan Singh, who led the project, said, “Terahertz technology […] can potentially boost intra-chip and inter-chip communication to support artificial intelligence and cloud-based technologies, such as interconnected self-driving cars, which will need to transmit data quickly to other nearby cars and infrastructure to navigate better and also to avoid accidents.”
Besides being used for AI and self-driving cars (and, of course, downloading hundreds of hours of video in seconds), 6G would also make a big difference for data centers, IoT devices, and long-range communications, among other applications.
Given that 5G networks are still in the process of being set up, though, 6G won’t be coming on the scene anytime soon; a recent whitepaper on 6G from Japanese company NTTDoCoMo estimates we’ll see it in 2030, pointing out that wireless connection tech generations have thus far been spaced about 10 years apart; we got 3G in the early 2000s, 4G in 2010, and 5G in 2020.
In the meantime, as 6G continues to develop, we’re still looking forward to the widespread adoption of 5G.
Image Credit: Hans Braxmeier from Pixabay Continue reading
Posted in Human Robots
#436180 Bipedal Robot Cassie Cal Learns to ...
There’s no particular reason why knowing how to juggle would be a useful skill for a robot. Despite this, robots are frequently taught how to juggle things. Blind robots can juggle, humanoid robots can juggle, and even drones can juggle. Why? Because juggling is hard, man! You have to think about a bunch of different things at once, and also do a bunch of different things at once, which this particular human at least finds to be overly stressful. While juggling may not stress robots out, it does require carefully coordinated sensing and computing and actuation, which means that it’s as good a task as any (and a more entertaining task than most) for testing the capabilities of your system.
UC Berkeley’s Cassie Cal robot, which consists of two legs and what could be called a torso if you were feeling charitable, has just learned to juggle by bouncing a ball on what would be her head if she had one of those. The idea is that if Cassie can juggle while balancing at the same time, she’ll be better able to do other things that require dynamic multitasking, too. And if that doesn’t work out, she’ll still be able to join the circus.
Cassie’s juggling is assisted by an external motion capture system that tracks the location of the ball, but otherwise everything is autonomous. Cassie is able to juggle the ball by leaning forwards and backwards, left and right, and moving up and down. She does this while maintaining her own balance, which is the whole point of this research—successfully executing two dynamic behaviors that may sometimes be at odds with one another. The end goal here is not to make a better juggling robot, but rather to explore dynamic multitasking, a skill that robots will need in order to be successful in human environments.
This work is from the Hybrid Robotics Lab at UC Berkeley, led by Koushil Sreenath, and is being done by Katherine Poggensee, Albert Li, Daniel Sotsaikich, Bike Zhang, and Prasanth Kotaru.
For a bit more detail, we spoke with Albert Li via email.
Image: UC Berkeley
UC Berkeley’s Cassie Cal getting ready to juggle.
IEEE Spectrum: What would be involved in getting Cassie to juggle without relying on motion capture?
Albert Li: Our motivation for starting off with motion capture was to first address the control challenge of juggling on a biped without worrying about implementing the perception. We actually do have a ball detector working on a camera, which would mean we wouldn’t have to rely on the motion capture system. However, we need to mount the camera in a way that it would provide the best upwards field of view, and we also have develop a reliable estimator. The estimator is particularly important because when the ball gets close enough to the camera, we actually can’t track the ball and have to assume our dynamic models describe its motion accurately enough until it bounces back up.
What keeps Cassie from juggling indefinitely?
There are a few factors that affect how long Cassie can sustain a juggle. While in simulation the paddle exhibits homogeneous properties like its stiffness and damping, in reality every surface has anisotropic contact properties. So, there are parts of the paddle which may be better for juggling than others (and importantly, react differently than modeled). These differences in contact are also exacerbated due to how the paddle is cantilevered when mounted on Cassie. When the ball hits these areas, it leads to a larger than expected error in a juggle. Due to the small size of the paddle, the ball may then just hit the paddle’s edge and end the juggling run. Over a very long run, this is a likely occurrence. Additionally, some large juggling errors could cause Cassie’s feet to slip slightly, which ends up changing the stable standing position over time. Since this version of the controller assumes Cassie is stationary, this change in position eventually leads to poor juggles and failure.
Would Cassie be able to juggle while walking (or hovershoe-ing)?
Walking (and hovershoe-ing) while juggling is a far more challenging problem and is certainly a goal for future research. Some of these challenges include getting the paddle to precise poses to juggle the ball while also moving to avoid any destabilizing effects of stepping incorrectly. The number of juggles per step of walking could also vary and make the mathematics of the problem more challenging. The controller goal is also more involved. While the current goal of the juggling controller is to juggle the ball to a static apex position, with a walking juggling controller, we may instead want to hit the ball forwards and also walk forwards to bounce it, juggle the ball along a particular path, etc. Solving such challenges would be the main thrusts of the follow-up research.
Can you give an example of a practical task that would be made possible by using a controller like this?
Studying juggling means studying contact behavior and leveraging our models of it to achieve a known objective. Juggling could also be used to study predictable post-contact flight behavior. Consider the scenario where a robot is attempting to make a catch, but fails, letting the ball to bounce off of its hand, and then recovering the catch. This behavior could also be intentional: It is often easier to first execute a bounce to direct the target and then perform a subsequent action. For example, volleyball players could in principle directly hit a spiked ball back, but almost always bump the ball back up and then return it.
Even beyond this motivating example, the kinds of models we employ to get juggling working are more generally applicable to any task that involves contact, which could include tasks besides bouncing like sliding and rolling. For example, clearing space on a desk by pushing objects to the side may be preferable than individually manipulating each and every object on it.
You mention collaborative juggling or juggling multiple balls—is that something you’ve tried yet? Can you talk a bit more about what you’re working on next?
We haven’t yet started working on collaborative or multi-ball juggling, but that’s also a goal for future work. Juggling multiple balls statically is probably the most reasonable next goal, but presents additional challenges. For instance, you have to encode a notion of juggling urgency (if the second ball isn’t hit hard enough, you have less time to get the first ball up before you get back to the second one).
On the other hand, collaborative human-robot juggling requires a more advanced decision-making framework. To get robust multi-agent juggling, the robot will need to employ some sort of probabilistic model of the expected human behavior (are they likely to move somewhere? Are they trying to catch the ball high or low? Is it safe to hit the ball back?). In general, developing such human models is difficult since humans are fairly unpredictable and often don’t exhibit rational behavior. This will be a focus of future work.
[ Hybrid Robotics Lab ] Continue reading
Posted in Human Robots
#436114 Video Friday: Transferring Human Motion ...
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!):
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.
We are very sad to say that MIT professor emeritus Woodie Flowers has passed away. Flowers will be remembered for (among many other things, like co-founding FIRST) the MIT 2.007 course that he began teaching in the mid-1970s, famous for its student competitions.
These competitions got a bunch of well-deserved publicity over the years; here’s one from 1985:
And the 2.007 competitions are still going strong—this year’s theme was Moonshot, and you can watch a replay of the event here.
[ MIT ]
Looks like Aibo is getting wireless integration with Hitachi appliances, which turns out to be pretty cute:
What is this magical box where you push a button and 60 seconds later fluffy pancakes come out?!
[ Aibo ]
LiftTiles are a “modular and reconfigurable room-scale shape display” that can turn your floor and walls into on-demand structures.
[ LiftTiles ]
Ben Katz, a grad student in MIT’s Biomimetics Robotics Lab, has been working on these beautiful desktop-sized Furuta pendulums:
That’s a crowdfunding project I’d pay way too much for.
[ Ben Katz ]
A clever bit of cable manipulation from MIT, using GelSight tactile sensors.
[ Paper ]
A useful display of industrial autonomy on ANYmal from the Oxford Robotics Group.
This video is of a demonstration for the ORCA Robotics Hub showing the ANYbotics ANYmal robot carrying out industrial inspection using autonomy software from Oxford Robotics Institute.
[ ORCA Hub ] via [ DRS ]
Thanks Maurice!
Meet Katie Hamilton, a software engineer at NASA’s Ames Research Center, who got into robotics because she wanted to help people with daily life. Katie writes code for robots, like Astrobee, who are assisting astronauts with routine tasks on the International Space Station.
[ NASA Astrobee ]
Transferring human motion to a mobile robotic manipulator and ensuring safe physical human-robot interaction are crucial steps towards automating complex manipulation tasks in human-shared environments. In this work we present a robot whole-body teleoperation framework for human motion transfer. We validate our approach through several experiments using the TIAGo robot, showing this could be an easy way for a non-expert to teach a rough manipulation skill to an assistive robot.
[ Paper ]
This is pretty cool looking for an autonomous boat, but we’ll see if they can build a real one by 2020 since at the moment it’s just an average rendering.
[ ProMare ]
I had no idea that asparagus grows like this. But, sure does make it easy for a robot to harvest.
[ Inaho ]
Skip to 2:30 in this Pepper unboxing video to hear the noise it makes when tickled.
[ HIT Lab NZ ]
In this interview, Jean Paul Laumond discusses his movement from mathematics to robotics and his career contributions to the field, especially in regards to motion planning and anthropomorphic motion. Describing his involvement at CNRS and in other robotics projects, such as HILARE, he comments on the distinction in perception between the robotics approach and a mathematics one.
[ IEEE RAS History ]
Here’s a couple of videos from the CMU Robotics Institute archives, showing some of the work that took place over the last few decades.
[ CMU RI ]
In this episode of the Artificial Intelligence Podcast, Lex Fridman speaks with David Ferrucci from IBM about Watson and (you guessed it) artificial intelligence.
David Ferrucci led the team that built Watson, the IBM question-answering system that beat the top humans in the world at the game of Jeopardy. He is also the Founder, CEO, and Chief Scientist of Elemental Cognition, a company working engineer AI systems that understand the world the way people do. This conversation is part of the Artificial Intelligence podcast.
[ AI Podcast ]
This week’s CMU RI Seminar is by Pieter Abbeel from UC Berkeley, on “Deep Learning for Robotics.”
Programming robots remains notoriously difficult. Equipping robots with the ability to learn would by-pass the need for what otherwise often ends up being time-consuming task specific programming. This talk will describe recent progress in deep reinforcement learning (robots learning through their own trial and error), in apprenticeship learning (robots learning from observing people), and in meta-learning for action (robots learning to learn). This work has led to new robotic capabilities in manipulation, locomotion, and flight, with the same approach underlying advances in each of these domains.
[ CMU RI ] Continue reading
Posted in Human Robots