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#437635 Toyota Research Demonstrates ...
Over the last several years, Toyota has been putting more muscle into forward-looking robotics research than just about anyone. In addition to the Toyota Research Institute (TRI), there’s that massive 175-acre robot-powered city of the future that Toyota still plans to build next to Mount Fuji. Even Toyota itself acknowledges that it might be crazy, but that’s just how they roll—as TRI CEO Gill Pratt told me a while back, when Toyota decides to do something, they really do go all-in on it.
TRI has been focusing heavily on home robots, which is reflective of the long-term nature of what TRI is trying to do, because home robots are both the place where we’ll need robots the most at the same time as they’re the place where it’s going to be hardest to deploy them. The unpredictable nature of homes, and the fact that homes tend to have squishy fragile people in them, are robot-unfriendly characteristics, but as the population continues to age (an increasingly acute problem in Japan), homes offer an enormous amount of potential for helping us maintain our independence.
Today, Toyota is showing off some of the research that it’s been working on recently, in the form of a virtual reality presentation in lieu of an in-person press event. For journalists, TRI pre-loaded the recording onto a VR headset, which was FedEx’ed to my house. You can watch the entire 40-minute presentation in 360 video on YouTube (or in VR if you have a headset of your own), but if you don’t watch the whole thing, you should at least check out the full-on GLaDOS (with arms) that TRI thinks belongs in your home.
The presentation features an introduction from Gill Pratt, who looks entirely too comfortable embedded inside of one of TRI’s telepresence robots. The event also covers a lot of territory, but the highlight is almost certainly the new hardware that TRI demonstrates.
Soft bubble gripper
Photo: TRI
This is a “soft bubble gripper,” under development at TRI’s Cambridge, Mass., branch. These passively-compliant, air-filled grippers make it easier to grasp many different kinds of objects safely, but the nifty thing is that they’ve got cameras inside of them watching a pattern of dots on the interior of the soft membrane.
When the outside of the bubble makes contact with an object, the bubble deforms, and the deformation of the dot pattern on the inside can be tracked by the camera to determine both directions and magnitudes of forces. This is a concept that we’ve seen elsewhere before, but TRI’s implementation is a clever way of making an inherently safe end effector that can still perform all the sensing you need it to do for relatively complex manipulation tasks.
The bubble gripper was presented at ICRA this year, and you can read the technical paper here.
Ceiling-mounted home robot
Photo: TRI
I don’t know whether robots dangling from the ceiling was somehow sinister pre-Portal, but it sure as heck is for me having played through that game a couple of times, and it’s since been reinforced by AUTO from WALL-E.
The reason that we generally see robots mounted on the floor or on tables or on mobile bases is that we’re bipeds, not bats, and giving a robot access to a human-like workspace is easiest to do if you also give that robot a human-like position and orientation. And if you want to be able to reach stuff high up, you do what TRI did with their previous generation of kitchen manipulator, and just give it the ability to make itself super tall. But TRI is convinced it’s a good place to put our future home robots:
One innovative concept is a “gantry robot” that would descend from an overhead framework to perform tasks such as loading the dishwasher, wiping surfaces, and clearing clutter. By traveling on the ceiling, the robot avoids the problems of navigating household floor clutter and navigating cramped spaces. When not in use, the robot would tuck itself up out of the way. To further investigate this idea, the team has built a laboratory prototype robot that can do all the same tasks as a floor-based mobile robot but with the innovative overhead mobility system.
Another obvious problem with the gantry robot is that you have to install all kinds of stuff in your ceiling for this to work, which makes it very impractical (if not totally impossible) to introduce a system like this into a home that wasn’t built specifically for it. If, however, you do build a home with a robot like this in mind, the animation below from TRI shows how it could be extra useful. Suddenly, stairs are a non-issue. Payload is presumably also a non-issue, since loads can be transferred to the ceiling. Batteries become unnecessary, so the whole robot can be much lighter weight, which in turn makes it safer. Sensors get a fantastic view, and obstacle avoidance becomes trivial.
Robots as “time machines”
Photo: TRI
TRI’s presentation covered more than what we’ve highlighted here—our focus has been on the hardware prototypes, but TRI had more to talk about, including learning through demonstration, scaling learning through simulation, and how TRI has been working with users to figure out what research directions should be explored. It’s all available right now on YouTube, and it’s well worth 40 minutes of your time.
“What we’re really focused on is this principle idea of amplifying, rather than replacing, human beings”
—Gill Pratt, TRI
It’s only been five years since Toyota announced the $1 billion investment that established TRI, and it feels like the progress that’s been made since then has been substantial. It’s not often that vision, resources, and long-term commitment come together like this, and TRI’s emphasis on making life better for people is one of the things that helps to keep us optimistic about the future of robotics.
“What we’re really focused on is this principle idea of amplifying, rather than replacing, human beings,” Gill Pratt told us. “And what it means to amplify a person, particularly as they’re aging—what we’re really trying to do is build a time machine. This may sound fanciful, and of course we can’t build a real time machine, but maybe we can build robotic assistants to make our lives as we age seem as if we are actually using a time machine.” He explains that it doesn’t mean building robots for convenience or to do our jobs for us. “It means building technology that enables us to continue to live and to work and to relate to each other as if we were younger,” he says. “And that’s really what our main goal is.” 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
#437596 IROS Robotics Conference Is Online Now ...
The 2020 International Conference on Intelligent Robots and Systems (IROS) was originally going to be held in Las Vegas this week. Like ICRA last spring, IROS has transitioned to a completely online conference, which is wonderful news: Now everyone everywhere can participate in IROS without having to spend a dime on travel.
IROS officially opened yesterday, and the best news is that registration is entirely free! We’ll take a quick look at what IROS has on offer this year, which includes some stuff that’s brand news to IROS.
Registration for IROS is super easy, and did we mention that it’s free? To register, just go here and fill out a quick and easy form. You don’t even have to be an IEEE Member or anything like that, although in our unbiased opinion, an IEEE membership is well worth it. Once you get the confirmation email, go to https://www.iros2020.org/ondemand/, put in the email address you used to register, and that’s it, you’ve got IROS!
Here are some highlights:
Plenaries and Keynotes
Without the normal space and time constraints, you won’t have to pick and choose between any of the three plenaries or 10 keynotes. Some of them are fancier than others, but we’re used to that sort of thing by now. It’s worth noting that all three plenaries (and three of the 10 keynotes) are given by extraordinarily talented women, which is excellent to see.
Technical Tracks
There are over 1,400 technical talks, divided up into 12 categories of 20 sessions each. Note that each of the 12 categories that you see on the main page can be scrolled through to show all 20 of the sessions; if there’s a bright red arrow pointing left or right you can scroll, and if the arrow is transparent, you’ve reached the end.
On the session page, you’ll see an autoplaying advertisement (that you can mute but not stop), below which each talk has a preview slide, a link to a ~15 minute presentation video, and another link to a PDF of the paper. No supplementary videos are available, which is a bit disappointing. While you can leave a comment on the video, there’s no way of interacting with the author(s) directly through the IROS site, so you’ll have to check the paper for an email address if you want to ask a question.
Award Finalists
IROS has thoughtfully grouped all of the paper award finalists together into nine sessions. These are some truly outstanding papers, and it’s worth watching these sessions even if you’re not interested in specific subject matter.
Workshops and Tutorials
This stuff is a little more impacted by asynchronicity and on-demandedness, and some of the workshops and tutorials have already taken place. But IROS has done a good job at collecting videos of everything and making them easy to access, and the dedicated websites for the workshops and tutorials themselves sometimes have more detailed info. If you’re having trouble finding where the workshops and tutorial section is, try the “Entrance” drop-down menu up at the top.
IROS Original Series
In place of social events and lab tours, IROS this year has come up with the “IROS Original Series,” which “hosts unique content that would be difficult to see at in-person events.” Right now, there are some interviews with a diverse group of interesting roboticists, and hopefully more will show up later on.
Enjoy!
Everything on the IROS On-Demand site should be available for at least the next month, so there’s no need to try and watch a thousand presentations over three days (which is what we normally have to do). So, relax, and enjoy yourself a bit by browsing all the options. And additional content will be made available over the next several weeks, so make sure to check back often to see what’s new.
[ IROS 2020 ] Continue reading →
Posted in Human Robots