Tag Archives: human
#437583 Video Friday: Attack of the Hexapod ...
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]
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.
Happy Halloween from HEBI Robotics!
Thanks Hardik!
[ HEBI Robotics ]
Happy Halloween from Berkshire Grey!
[ Berkshire Grey ]
These are some preliminary results of our lab’s new work on using reinforcement learning to train neural networks to imitate common bipedal gait behaviors, without using any motion capture data or reference trajectories. Our method is described in an upcoming submission to ICRA 2021. Work by Jonah Siekmann and Yesh Godse.
[ OSU DRL ]
The northern goshawk is a fast, powerful raptor that flies effortlessly through forests. This bird was the design inspiration for the next-generation drone developed by scientifics of the Laboratory of Intelligent Systems of EPFL led by Dario Floreano. They carefully studied the shape of the bird’s wings and tail and its flight behavior, and used that information to develop a drone with similar characteristics.
The engineers already designed a bird-inspired drone with morphing wing back in 2016. In a step forward, their new model can adjust the shape of its wing and tail thanks to its artificial feathers. Flying this new type of drone isn’t easy, due to the large number of wing and tail configurations possible. To take full advantage of the drone’s flight capabilities, Floreano’s team plans to incorporate artificial intelligence into the drone’s flight system so that it can fly semi-automatically. The team’s research has been published in Science Robotics.
[ EPFL ]
Oopsie.
[ Roborace ]
We’ve covered MIT’s Roboats in the past, but now they’re big enough to keep a couple of people afloat.
Self-driving boats have been able to transport small items for years, but adding human passengers has felt somewhat intangible due to the current size of the vessels. Roboat II is the “half-scale” boat in the growing body of work, and joins the previously developed quarter-scale Roboat, which is 1 meter long. The third installment, which is under construction in Amsterdam and is considered to be “full scale,” is 4 meters long and aims to carry anywhere from four to six passengers.
[ MIT ]
With a training technique commonly used to teach dogs to sit and stay, Johns Hopkins University computer scientists showed a robot how to teach itself several new tricks, including stacking blocks. With the method, the robot, named Spot, was able to learn in days what typically takes a month.
[ JHU ]
Exyn, a pioneer in autonomous aerial robot systems for complex, GPS-denied industrial environments, today announced the first dog, Kody, to successfully fly a drone at Number 9 Coal Mine, in Lansford, PA. Selected to carry out this mission was the new autonomous aerial robot, the ExynAero.
Yes, this is obviously a publicity stunt, and Kody is only flying the drone in the sense that he’s pushing the launch button and then taking a nap. But that’s also the point— drone autonomy doesn’t get much fuller than this, despite the challenge of the environment.
[ Exyn ]
In this video object instance segmentation and shape completion are combined with classical regrasp planning to perform pick-place of novel objects. It is demonstrated with a UR5, Robotiq 85 parallel-jaw gripper, and Structure depth sensor with three rearrangement tasks: bin packing (minimize the height of the packing), placing bottles onto coasters, and arrange blocks from tallest to shortest (according to the longest edge). The system also accounts for uncertainty in the segmentation/completion by avoiding grasping or placing on parts of the object where perceptual uncertainty is predicted to be high.
[ Paper ] via [ Northeastern ]
Thanks Marcus!
U can’t touch this!
[ University of Tokyo ]
We introduce a way to enable more natural interaction between humans and robots through Mixed Reality, by using a shared coordinate system. Azure Spatial Anchors, which already supports colocalizing multiple HoloLens and smartphone devices in the same space, has now been extended to support robots equipped with cameras. This allows humans and robots sharing the same space to interact naturally: humans can see the plan and intention of the robot, while the robot can interpret commands given from the person’s perspective. We hope that this can be a building block in the future of humans and robots being collaborators and coworkers.
[ Microsoft ]
Some very high jumps from the skinniest quadruped ever.
[ ODRI ]
In this video we present recent efforts to make our humanoid robot LOLA ready for multi-contact locomotion, i.e. additional hand-environment support for extra stabilization during walking.
[ TUM ]
Classic bike moves from Dr. Guero.
[ Dr. Guero ]
For a robotics company, iRobot is OLD.
[ iRobot ]
The Canadian Space Agency presents Juno, a preliminary version of a rover that could one day be sent to the Moon or Mars. Juno can navigate autonomously or be operated remotely. The Lunar Exploration Analogue Deployment (LEAD) consisted in replicating scenarios of a lunar sample return mission.
[ CSA ]
How exactly does the Waymo Driver handle a cat cutting across its driving path? Jonathan N., a Product Manager on our Perception team, breaks it all down for us.
Now do kangaroos.
[ Waymo ]
Jibo is hard at work at MIT playing games with kids.
Children’s creativity plummets as they enter elementary school. Social interactions with peers and playful environments have been shown to foster creativity in children. Digital pedagogical tools often lack the creativity benefits of co-located social interaction with peers. In this work, we leverage a social embodied robot as a playful peer and designed Escape!Bot, a game involving child-robot co-play, where the robot is a social agent that scaffolds for creativity during gameplay.
[ Paper ]
It’s nice when convenience stores are convenient even for the folks who have to do the restocking.
Who’s moving the crates around, though?
[ Telexistence ]
Hi, fans ! Join the ROS World 2020, opening November 12th , and see the footage of ROBOTIS’ ROS platform robots 🙂
[ ROS World 2020 ]
ML/RL methods are often viewed as a magical black box, and while that’s not true, learned policies are nonetheless a valuable tool that can work in conjunction with the underlying physics of the robot. In this video, Agility CTO Jonathan Hurst – wearing his professor hat at Oregon State University – presents some recent student work on using learned policies as a control method for highly dynamic legged robots.
[ Agility Robotics ]
Here’s an ICRA Legged Robots workshop talk from Marco Hutter at ETH Zürich, on Autonomy for ANYmal.
Recent advances in legged robots and their locomotion skills has led to systems that are skilled and mature enough for real-world deployment. In particular, quadrupedal robots have reached a level of mobility to navigate complex environments, which enables them to take over inspection or surveillance jobs in place like offshore industrial plants, in underground areas, or on construction sites. In this talk, I will present our research work with the quadruped ANYmal and explain some of the underlying technologies for locomotion control, environment perception, and mission autonomy. I will show how these robots can learn and plan complex maneuvers, how they can navigate through unknown environments, and how they are able to conduct surveillance, inspection, or exploration scenarios.
[ RSL ] Continue reading →
Posted in Human Robots
#437579 Disney Research Makes Robotic Gaze ...
While it’s not totally clear to what extent human-like robots are better than conventional robots for most applications, one area I’m personally comfortable with them is entertainment. The folks over at Disney Research, who are all about entertainment, have been working on this sort of thing for a very long time, and some of their animatronic attractions are actually quite impressive.
The next step for Disney is to make its animatronic figures, which currently feature scripted behaviors, to perform in an interactive manner with visitors. The challenge is that this is where you start to get into potential Uncanny Valley territory, which is what happens when you try to create “the illusion of life,” which is what Disney (they explicitly say) is trying to do.
In a paper presented at IROS this month, a team from Disney Research, Caltech, University of Illinois at Urbana-Champaign, and Walt Disney Imagineering is trying to nail that illusion of life with a single, and perhaps most important, social cue: eye gaze.
Before you watch this video, keep in mind that you’re watching a specific character, as Disney describes:
The robot character plays an elderly man reading a book, perhaps in a library or on a park bench. He has difficulty hearing and his eyesight is in decline. Even so, he is constantly distracted from reading by people passing by or coming up to greet him. Most times, he glances at people moving quickly in the distance, but as people encroach into his personal space, he will stare with disapproval for the interruption, or provide those that are familiar to him with friendly acknowledgment.
What, exactly, does “lifelike” mean in the context of robotic gaze? The paper abstract describes the goal as “[seeking] to create an interaction which demonstrates the illusion of life.” I suppose you could think of it like a sort of old-fashioned Turing test focused on gaze: If the gaze of this robot cannot be distinguished from the gaze of a human, then victory, that’s lifelike. And critically, we’re talking about mutual gaze here—not just a robot gazing off into the distance, but you looking deep into the eyes of this robot and it looking right back at you just like a human would. Or, just like some humans would.
The approach that Disney is using is more animation-y than biology-y or psychology-y. In other words, they’re not trying to figure out what’s going on in our brains to make our eyes move the way that they do when we’re looking at other people and basing their control system on that, but instead, Disney just wants it to look right. This “visual appeal” approach is totally fine, and there’s been an enormous amount of human-robot interaction (HRI) research behind it already, albeit usually with less explicitly human-like platforms. And speaking of human-like platforms, the hardware is a “custom Walt Disney Imagineering Audio-Animatronics bust,” which has DoFs that include neck, eyes, eyelids, and eyebrows.
In order to decide on gaze motions, the system first identifies a person to target with its attention using an RGB-D camera. If more than one person is visible, the system calculates a curiosity score for each, currently simplified to be based on how much motion it sees. Depending on which person that the robot can see has the highest curiosity score, the system will choose from a variety of high level gaze behavior states, including:
Read: The Read state can be considered the “default” state of the character. When not executing another state, the robot character will return to the Read state. Here, the character will appear to read a book located at torso level.
Glance: A transition to the Glance state from the Read or Engage states occurs when the attention engine indicates that there is a stimuli with a curiosity score […] above a certain threshold.
Engage: The Engage state occurs when the attention engine indicates that there is a stimuli […] to meet a threshold and can be triggered from both Read and Glance states. This state causes the robot to gaze at the person-of-interest with both the eyes and head.
Acknowledge: The Acknowledge state is triggered from either Engage or Glance states when the person-of-interest is deemed to be familiar to the robot.
Running underneath these higher level behavior states are lower level motion behaviors like breathing, small head movements, eye blinking, and saccades (the quick eye movements that occur when people, or robots, look between two different focal points). The term for this hierarchical behavioral state layering is a subsumption architecture, which goes all the way back to Rodney Brooks’ work on robots like Genghis in the 1980s and Cog and Kismet in the ’90s, and it provides a way for more complex behaviors to emerge from a set of simple, decentralized low-level behaviors.
“25 years on Disney is using my subsumption architecture for humanoid eye control, better and smoother now than our 1995 implementations on Cog and Kismet.”
—Rodney Brooks, MIT emeritus professor
Brooks, an emeritus professor at MIT and, most recently, cofounder and CTO of Robust.ai, tweeted about the Disney project, saying: “People underestimate how long it takes to get from academic paper to real world robotics. 25 years on Disney is using my subsumption architecture for humanoid eye control, better and smoother now than our 1995 implementations on Cog and Kismet.”
From the paper:
Although originally intended for control of mobile robots, we find that the subsumption architecture, as presented in [17], lends itself as a framework for organizing animatronic behaviors. This is due to the analogous use of subsumption in human behavior: human psychomotor behavior can be intuitively modeled as layered behaviors with incoming sensory inputs, where higher behavioral levels are able to subsume lower behaviors. At the lowest level, we have involuntary movements such as heartbeats, breathing and blinking. However, higher behavioral responses can take over and control lower level behaviors, e.g., fight-or-flight response can induce faster heart rate and breathing. As our robot character is modeled after human morphology, mimicking biological behaviors through the use of a bottom-up approach is straightforward.
The result, as the video shows, appears to be quite good, although it’s hard to tell how it would all come together if the robot had more of, you know, a face. But it seems like you don’t necessarily need to have a lifelike humanoid robot to take advantage of this architecture in an HRI context—any robot that wants to make a gaze-based connection with a human could benefit from doing it in a more human-like way.
“Realistic and Interactive Robot Gaze,” by Matthew K.X.J. Pan, Sungjoon Choi, James Kennedy, Kyna McIntosh, Daniel Campos Zamora, Gunter Niemeyer, Joohyung Kim, Alexis Wieland, and David Christensen from Disney Research, California Institute of Technology, University of Illinois at Urbana-Champaign, and Walt Disney Imagineering, was presented at IROS 2020. You can find the full paper, along with a 13-minute video presentation, on the IROS on-demand conference website.
< Back to IEEE Journal Watch Continue reading →
Posted in Human Robots
#437575 AI-Directed Robotic Hand Learns How to ...
Reaching for a nearby object seems like a mindless task, but the action requires a sophisticated neural network that took humans millions of years to evolve. Now, robots are acquiring that same ability using artificial neural networks. In a recent study, a robotic hand “learns” to pick up objects of different shapes and hardness using three different grasping motions.
The key to this development is something called a spiking neuron. Like real neurons in the brain, artificial neurons in a spiking neural network (SNN) fire together to encode and process temporal information. Researchers study SNNs because this approach may yield insights into how biological neural networks function, including our own.
“The programming of humanoid or bio-inspired robots is complex,” says Juan Camilo Vasquez Tieck, a research scientist at FZI Forschungszentrum Informatik in Karlsruhe, Germany. “And classical robotics programming methods are not always suitable to take advantage of their capabilities.”
Conventional robotic systems must perform extensive calculations, Tieck says, to track trajectories and grasp objects. But a robotic system like Tieck’s, which relies on a SNN, first trains its neural net to better model system and object motions. After which it grasps items more autonomously—by adapting to the motion in real-time.
The new robotic system by Tieck and his colleagues uses an existing robotic hand, called a Schunk SVH 5-finger hand, which has the same number of fingers and joints as a human hand.
The researchers incorporated a SNN into their system, which is divided into several sub-networks. One sub-network controls each finger individually, either flexing or extending the finger. Another concerns each type of grasping movement, for example whether the robotic hand will need to do a pinching, spherical or cylindrical movement.
For each finger, a neural circuit detects contact with an object using the currents of the motors and the velocity of the joints. When contact with an object is detected, a controller is activated to regulate how much force the finger exerts.
“This way, the movements of generic grasping motions are adapted to objects with different shapes, stiffness and sizes,” says Tieck. The system can also adapt its grasping motion quickly if the object moves or deforms.
The robotic grasping system is described in a study published October 24 in IEEE Robotics and Automation Letters. The researchers’ robotic hand used its three different grasping motions on objects without knowing their properties. Target objects included a plastic bottle, a soft ball, a tennis ball, a sponge, a rubber duck, different balloons, a pen, and a tissue pack. The researchers found, for one, that pinching motions required more precision than cylindrical or spherical grasping motions.
“For this approach, the next step is to incorporate visual information from event-based cameras and integrate arm motion with SNNs,” says Tieck. “Additionally, we would like to extend the hand with haptic sensors.”
The long-term goal, he says, is to develop “a system that can perform grasping similar to humans, without intensive planning for contact points or intense stability analysis, and [that is] able to adapt to different objects using visual and haptic feedback.” Continue reading →
Posted in Human Robots
#437564 How We Won the DARPA SubT Challenge: ...
This is a guest post. The views expressed here are those of the authors and do not necessarily represent positions of IEEE or its organizational units.
“Do you smell smoke?” It was three days before the qualification deadline for the Virtual Tunnel Circuit of the DARPA Subterranean Challenge Virtual Track, and our team was barrelling through last-minute updates to our robot controllers in a small conference room at the Michigan Tech Research Institute (MTRI) offices in Ann Arbor, Mich. That’s when we noticed the smell. We’d assumed that one of the benefits of entering a virtual disaster competition was that we wouldn’t be exposed to any actual disasters, but equipment in the basement of the building MTRI shares had started to smoke. We evacuated. The fire department showed up. And as soon as we could, the team went back into the building, hunkered down, and tried to make up for the unexpected loss of several critical hours.
Team BARCS joins the SubT Virtual Track
The smoke incident happened more than a year after we first learned of the DARPA Subterranean Challenge. DARPA announced SubT early in 2018, and at that time, we were interested in building internal collaborations on multi-agent autonomy problems, and SubT seemed like the perfect opportunity. Though a few of us had backgrounds in robotics, the majority of our team was new to the field. We knew that submitting a proposal as a largely non-traditional robotics team from an organization not known for research in robotics was a risk. However, the Virtual Track gave us the opportunity to focus on autonomy and multi-agent teaming strategies, areas requiring skill in asynchronous computing and sensor data processing that are strengths of our Institute. The prevalence of open source code, small inexpensive platforms, and customizable sensors has provided the opportunity for experts in fields other than robotics to apply novel approaches to robotics problems. This is precisely what makes the Virtual Track of SubT appealing to us, and since starting SubT, autonomy has developed into a significant research thrust for our Institute. Plus, robots are fun!
After many hours of research, discussion, and collaboration, we submitted our proposal early in 2018. And several months later, we found out that we had won a contract and became a funded team (Team BARCS) in the SubT Virtual Track. Now we needed to actually make our strategy work for the first SubT Tunnel Circuit competition, taking place in August of 2019.
Building a team of virtual robots
A natural approach to robotics competitions like SubT is to start with the question of “what can X-type robot do” and then build a team and strategy around individual capabilities. A particular challenge for the SubT Virtual Track is that we can’t design our own systems; instead, we have to choose from a predefined set of simulated robots and sensors that DARPA provides, based on the real robots used by Systems Track teams. Our approach is to look at what a team of robots can do together, determining experimentally what the best team configuration is for each environment. By the final competition, ideally we will be demonstrating the value of combining platforms across multiple Systems Track teams into a single Virtual Track team. Each of the robot configurations in the competition has an associated cost, and team size is constrained by a total cost. This provides another impetus for limiting dependence on complex sensor packages, though our ranging preference is 3D lidar, which is the most expensive sensor!
Image: Michigan Tech Research Institute
The teams can rely on realistic physics and sensors but they start off with no maps of any kind, so the focus is on developing autonomous exploratory behavior, navigation methods, and object recognition for their simulated robots.
One of the frequent questions we receive about the Virtual Track is if it’s like a video game. While it may look similar on the surface, everything under the hood in a video game is designed to service the game narrative and play experience, not require novel research in AI and autonomy. The purpose of simulations, on the other hand, is to include full physics and sensor models (including noise and errors) to provide a testbed for prototyping and developing solutions to those real-world challenges. We are starting with realistic physics and sensors but no maps of any kind, so the focus is on developing autonomous exploratory behavior, navigation methods, and object recognition for our simulated robots.
Though the simulation is more like real life than a video game, it is not real life. Due to occasional software bugs, there are still non-physical events, like the robots falling through an invisible hole in the world or driving through a rock instead of over it or flipping head over heels when driving over a tiny lip between world tiles. These glitches, while sometimes frustrating, still allow the SubT Virtual platform to be realistic enough to support rapid prototyping of controller modules that will transition straightforwardly onto hardware, closing the loop between simulation and real-world robots.
Full autonomy for DARPA-hard scenarios
The Virtual Track requirement that the robotic agents be fully autonomous, rather than have a human supervisor, is a significant distinction between the Systems and Virtual Tracks of SubT. Our solutions must be hardened against software faults caused by things like missing and bad data since our robots can’t turn to us for help. In order for a team of robots to complete this objective reliably with no human-in-the-loop, all of the internal systems, from perception to navigation to control to actuation to communications, must be able to autonomously identify and manage faults and failures anywhere in the control chain.
The communications limitations in subterranean environments (both real and virtual) mean that we need to keep the amount of information shared between robots low, while making the usability of that information for joint decision-making high. This goal has guided much of our design for autonomous navigation and joint search strategy for our team. For example, instead of sharing the full SLAM map of the environment, our agents only share a simplified graphical representation of the space, along with data about frontiers it has not yet explored, and are able to merge its information with the graphs generated by other agents. The merged graph can then be used for planning and navigation without having full knowledge of the detailed 3D map.
The Virtual Track requires that the robotic agents be fully autonomous. With no human-in-the-loop, all of the internal systems, from perception to navigation to control to actuation to communications, must be able to identify and manage faults and failures anywhere in the control chain.
Since the objective of the SubT program is to advance the state-of-the-art in rapid autonomous exploration and mapping of subterranean environments by robots, our first software design choices focused on the mapping task. The SubT virtual environments are sufficiently rich as to provide interesting problems in building so-called costmaps that accurately separate obstructions that are traversable (like ramps) from legitimately impassible obstructions. An extra complication we discovered in the first course, which took place in mining tunnels, was that the angle of the lowest beam of the lidar was parallel to the down ramps in the tunnel environment, so they could not “see” the ground (or sometimes even obstructions on the ramp) until they got close enough to the lip of the ramp to receive lidar reflections off the bottom of the ramp. In this case, we had to not only change the costmap to convince the robot that there was safe ground to reach over the lip of the ramp, but also had to change the path planner to get the robot to proceed with caution onto the top of the ramp in case there were previously unseen obstructions on the ramp.
In addition to navigation in the costmaps, the robot must be able to generate its own goals to navigate to. This is what produces exploratory behavior when there is no map to start with. SLAM is used to generate a detailed map of the environment explored by a single robot—the space it has probed with its sensors. From the sensor data, we are able to extract information about the interior space of the environment while looking for holes in the data, to determine things like whether the current tunnel continues or ends, or how many tunnels meet at an intersection. Once we have some understanding of the interior space, we can place navigation goals in that space. These goals naturally update as the robot traverses the tunnel, allowing the entire space to be explored.
Sending our robots into the virtual unknown
The solutions for the Virtual Track competitions are tested by DARPA in multiple sequestered runs across many environments for each Circuit in the month prior to the Systems Track competition. We must wait until the joint award ceremony at the conclusion of the Systems Track to find out the results, and we are completely in the dark about placings before the awards are announced. It’s nerve-wracking! The challenges of the worlds used in the Circuit events are also hand-designed, so features of the worlds we use for development could be combined in ways we have not anticipated—it’s always interesting to see what features were prioritized after the event. We test everything in our controllers well enough to feel confident that we at least are submitting something reasonably stable and broadly capable, and once the solution is in, we can’t really do anything other than “let go” and get back to work on the next phase of development. Maybe it’s somewhat like sending your kid to college: “we did our best to prepare you for this world, little bots. Go do good.”
Image: Michigan Tech Research Institute
The first SubT competition was the Tunnel Circuit, featuring a labyrinthine environment that simulated human-engineered tunnels, including hazards such as vertical shafts and rubble.
The first competition was the Tunnel Circuit, in October 2019. This environment models human-engineered tunnels. Two substantial challenges in this environment were vertical shafts and rubble. Our team accrued 21 points over 15 competition runs in five separate tunnel environments for a second place finish, behind Team Coordinated Robotics.
The next phase of the SubT virtual competition was the Urban Circuit. Much of the difference between our Tunnel and Urban Circuit results came down to thorough testing to identify failure modes and implementations of checks and data filtering for fault tolerance. For example, in the SLAM nodes run by a single robot, the coordinates of the most recent sensor data are changed multiple times during processing and integration into the current global 3D map of the “visited” environment stored by that robot. If there is lag in IMU or clock data, the observation may be temporarily registered at a default location that is very far from the actual position. Since most of our decision processes for exploration are downstream from SLAM, this can cause faulty or impossible goals to be generated, and the robots then spend inordinate amounts of time trying to drive through walls. We updated our method to add a check to see if the new map position has jumped a far distance from the prior map position, and if so, we threw that data out.
Image: Michigan Tech Research Institute
In open spaces like the rooms in the Urban circuit, we adjusted our approach to exploration through graph generation to allow the robots to accurately identify viable routes while helping to prevent forays off platform edges.
Our approach to exploration through graph generation based on identification of interior spaces allowed us to thoroughly explore the centers of rooms, although we did have to make some changes from the Tunnel circuit to achieve that. In the Tunnel circuit, we used a simplified graph of the environment based on landmarks like intersections. The advantage of this approach is that it is straightforward for two robots to compare how the graphs of the space they explored individually overlap. In open spaces like the rooms in the Urban circuit, we chose to instead use a more complex, less directly comparable graph structure based on the individual robot’s trajectory. This allowed the robots to accurately identify viable routes between features like subway station platforms and subway tracks, as well as to build up the navigation space for room interiors, while helping to prevent forays off the platform edges. Frontier information is also integrated into the graph, providing a uniform data structure for both goal selection and route planning.
The results are in!
The award ceremony for the Urban Circuit was held concurrently with the Systems Track competition awards this past February in Washington State. We sent a team representative to participate in the Technical Interchange Meeting and present the approach for our team, and the rest of us followed along from our office space on the DARPAtv live stream. While we were confident in our solution, we had also been tracking the online leaderboard and knew our competitors were going to be submitting strong solutions. Since the competition environments are hand-designed, there are always novel challenges that could be presented in these environments as well. We knew we would put up a good fight, but it was very exciting to see BARCS appear in first place!
Any time we implement a new module in our control system, there is a lot of parameter tuning that has to happen to produce reliably good autonomous behavior. In the Urban Circuit, we did not sufficiently test some parameter values in our exploration modules. The effect of this was that the robots only chose to go down small hallways after they explored everything else in their environment, which meant very often they ran out of time and missed a lot of small rooms. This may be the biggest source of lost points for us in the Urban Circuit. One of our major plans going forward from the Urban Circuit is to integrate more sophisticated node selection methods, which can help our robots more intelligently prioritize which frontier nodes to visit. By going through all three Circuit challenges, we will learn how to appropriately add weights to the frontiers based on features of the individual environments. For the Final Challenge, when all three Circuit environments will be combined into large systems, we plan to implement adaptive controllers that will identify their environments and use the appropriate optimized parameters for that environment. In this way, we expect our agents to be able to (for example) prioritize hallways and other small spaces in Urban environments, and perhaps prioritize large openings over small in the Cave environments, if the small openings end up being treacherous overall.
Next for our team: Cave Circuit
Coming up next for Team BARCS is the Virtual Cave Circuit. We are in the middle of testing our hypothesis that our controller will transition from UGVs to UAVs and developing strategies for refining our solution to handle Cave Circuit environmental hazards. The UAVs have a shorter battery life than the UGVs, so executing a joint exploration strategy will also be a high priority for this event, as will completing our work on graph sharing and merging, which will give our robot teams more sophisticated options for navigation and teamwork. We’re reaching a threshold in development where we can start increasing the “smarts” of the robots, which we anticipate will be critical for the final competition, where all of the challenges of SubT will be combined to push the limits of innovation. The Cave Circuit will also have new environmental challenges to tackle: dynamic features such as rock falls have been added, which will block previously accessible passages in the cave environment. We think our controllers are well-poised to handle this new challenge, and we’re eager to find out if that’s the case.
As of now, the biggest worries for us are time and team composition. The Cave Circuit deadline has been postponed to October 15 due to COVID-19 delays, with the award ceremony in mid-November, but there have also been several very compelling additions to the testbed that we would like to experiment with before submission, including droppable networking ‘breadcrumbs’ and new simulated platforms. There are design trade-offs when balancing general versus specialist approaches to the controllers for these robots—since we are adding UAVs to our team for the first time, there are new decisions that will have to be made. For example, the UAVs can ascend into vertical spaces, but only have a battery life of 20 minutes. The UGVs by contrast have 90 minute battery life. One of our strategies is to do an early return to base with one or more agents to buy down risk on making any artifact reports at all for the run, hedging against our other robots not making it back in time, a lesson learned from the Tunnel Circuit. Should a UAV take on this role, or is it better to have them explore deeper into the environment and instead report their artifacts to a UGV or network node, which comes with its own risks? Testing and experimentation to determine the best options takes time, which is always a worry when preparing for a competition! We also anticipate new competitors and stiffer competition all around.
Image: Michigan Tech Research Institute
Team BARCS has now a year to prepare for the final DARPA SubT Challenge event, expected to take place in late 2021.
Going forward from the Cave Circuit, we will have a year to prepare for the final DARPA SubT Challenge event, expected to take place in late 2021. What we are most excited about is increasing the level of intelligence of the agents in their teamwork and joint exploration of the environment. Since we will have (hopefully) built up robust approaches to handling each of the specific types of environments in the Tunnel, Urban, and Cave circuits, we will be aiming to push the limits on collaboration and efficiency among the agents in our team. We view this as a central research contribution of the Virtual Track to the Subterranean Challenge because intelligent, adaptive, multi-robot collaboration is an upcoming stage of development for integration of robots into our lives.
The Subterranean Challenge Virtual Track gives us a bridge for transitioning our more abstract research ideas and algorithms relevant to this degree of autonomy and collaboration onto physical systems, and exploring the tangible outcomes of implementing our work in the real world. And the next time there’s an incident in the basement of our building, the robots (and humans) of Team BARCS will be ready to respond.
Richard Chase, Ph.D., P.E., is a research scientist at Michigan Tech Research Institute (MTRI) and has 20 years of experience developing robotics and cyber physical systems in areas from remote sensing to autonomous vehicles. At MTRI, he works on a variety of topics such as swarm autonomy, human-swarm teaming, and autonomous vehicles. His research interests are the intersection of design, robotics, and embedded systems.
Sarah Kitchen is a Ph.D. mathematician working as a research scientist and an AI/Robotics focus area leader at MTRI. Her research interests include intelligent autonomous agents and multi-agent collaborative teams, as well as applications of autonomous robots to sensing systems.
This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001118C0124 and is released under Distribution Statement (Approved for Public Release, Distribution Unlimited). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA. Continue reading →
Posted in Human Robots
#437562 Video Friday: Aquanaut Robot Takes to ...
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]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
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.
To prepare the Perseverance rover for its date with Mars, NASA’s Mars 2020 mission team conducted a wide array of tests to help ensure a successful entry, descent and landing at the Red Planet. From parachute verification in the world’s largest wind tunnel, to hazard avoidance practice in Death Valley, California, to wheel drop testing at NASA’s Jet Propulsion Laboratory and much more, every system was put through its paces to get ready for the big day. The Perseverance rover is scheduled to land on Mars on February 18, 2021.
[ JPL ]
Awesome to see Aquanaut—the “underwater transformer” we wrote about last year—take to the ocean!
Also their new website has SHARKS on it.
[ HMI ]
Nature has inspired engineers at UNSW Sydney to develop a soft fabric robotic gripper which behaves like an elephant's trunk to grasp, pick up and release objects without breaking them.
[ UNSW ]
Collaborative robots offer increased interaction capabilities at relatively low cost but, in contrast to their industrial counterparts, they inevitably lack precision. We address this problem by relying on a dual-arm system with laser-based sensing to measure relative poses between objects of interest and compensate for pose errors coming from robot proprioception.
[ Paper ]
Developed by NAVER LABS, with Korea University of Technology & Education (Koreatech), the robot arm now features an added waist, extending the available workspace, as well as a sensor head that can perceive objects. It has also been equipped with a robot hand “BLT Gripper” that can change to various grasping methods.
[ NAVER Labs ]
In case you were still wondering why SoftBank acquired Aldebaran and Boston Dynamics:
[ RobotStart ]
DJI's new Mini 2 drone is here with a commercial so hip it makes my teeth scream.
[ DJI ]
Using simple materials, such as plastic struts and cardboard rolls, the first prototype of the RBO Hand 3 is already capable of grasping a large range of different objects thanks to its opposable thumb.
The RBO Hand 3 performs an edge grasp before handing-over the object to a person. The hand actively exploits constraints in the environment (the tabletop) for grasping the object. Thanks to its compliance, this interaction is safe and robust.
[ TU Berlin ]
Flyability's Elios 2 helped researchers inspect Reactor Five at the Chernobyl nuclear disaster site in order to determine whether any uranium was present. Prior to this mission, Reactor Five had not been investigated since the disaster in April of 1986.
[ Flyability ]
Thanks Zacc!
SOTO 2 is here! Together with our development partners from the industry, we have greatly enhanced the SOTO prototype over the last two years. With the new version of the robot, Industry 4.0 will become a great deal more real: SOTO brings materials to the assembly line, just-in-time and completely autonomously.
[ Magazino ]
A drone that can fly sustainably for long distances over land and water, and can land almost anywhere, will be able to serve a wide range of applications. There are already drones that fly using ‘green’ hydrogen, but they either fly very slowly or cannot land vertically. That’s why researchers at TU Delft, together with the Royal Netherlands Navy and the Netherlands Coastguard, developed a hydrogen-powered drone that is capable of vertical take-off and landing whilst also being able to fly horizontally efficiently for several hours, much like regular aircraft. The drone uses a combination of hydrogen and batteries as its power source.
[ MAVLab ]
The National Nuclear User Facility for Hot Robotics (NNUF-HR) is an EPSRC funded facility to support UK academia and industry to deliver ground-breaking, impactful research in robotics and artificial intelligence for application in extreme and challenging nuclear environments.
[ NNUF ]
At the Karolinska University Laboratory in Sweden, an innovation project based around an ABB collaborative robot has increased efficiency and created a better working environment for lab staff.
[ ABB ]
What I find interesting about DJI's enormous new agricultural drone is that it's got a spinning obstacle detecting sensor that's a radar, not a lidar.
Also worth noting is that it seems to detect the telephone pole, but not the support wire that you can see in the video feed, although the visualization does make it seem like it can spot the power lines above.
[ DJI ]
Josh Pieper has spend the last year building his own quadruped, and you can see what he's been up to in just 12 minutes.
[ mjbots ]
Thanks Josh!
Dr. Ryan Eustice, TRI Senior Vice President of Automated Driving, delivers a keynote speech — “The Road to Vehicle Automation, a Toyota Guardian Approach” — to SPIE's Future Sensing Technologies 2020. During the presentation, Eustice provides his perspective on the current state of automated driving, summarizes TRI's Guardian approach — which amplifies human drivers, rather than replacing them — and summarizes TRI's recent developments in core AD capabilities.
[ TRI ]
Two excellent talks this week from UPenn GRASP Lab, from Ruzena Bajcsy and Vijay Kumar.
A panel discussion on the future of robotics and societal challenges with Dr. Ruzena Bajcsy as a Roboticist and Founder of the GRASP Lab.
In this talk I will describe the role of the White House Office of Science and Technology Policy in supporting science and technology research and education, and the lessons I learned while serving in the office. I will also identify a few opportunities at the intersection of technology and policy and broad societal challenges.
[ UPenn ]
The IROS 2020 “Perception, Learning, and Control for Autonomous Agile Vehicles” workshop is all online—here's the intro, but you can click through for a playlist that includes videos of the entire program, and slides are available as well.
[ NYU ] Continue reading →
Posted in Human Robots