Feed aggregator

Contemporary research in human-machine symbiosis has mainly concentrated on enhancing relevant sensory, perceptual, and motor capacities, assuming short-term and nearly momentary interaction sessions. Still, human-machine confluence encompasses an inherent temporal dimension that is typically overlooked. The present work shifts the focus on the temporal and long-lasting aspects of symbiotic human-robot interaction (sHRI). We explore the integration of three time-aware modules, each one focusing on a diverse part of the sHRI timeline. Specifically, the Episodic Memory considers past experiences, the Generative Time Models estimate the progress of ongoing activities, and the Daisy Planner devices plans for the timely accomplishment of goals. The integrated system is employed to coordinate the activities of a multi-agent team. Accordingly, the proposed system (i) predicts human preferences based on past experience, (ii) estimates performance profile and task completion time, by monitoring human activity, and (iii) dynamically adapts multi-agent activity plans to changes in expectation and Human-Robot Interaction (HRI) performance. The system is deployed and extensively assessed in real-world and simulated environments. The obtained results suggest that building upon the unfolding and the temporal properties of team tasks can significantly enhance the fluency of sHRI.

In this paper, a new scheme for multi-lateral remote rehabilitation is proposed. There exist one therapist, one patient, and several trainees, who are participating in the process of telerehabilitation (TR) in this scheme. This kind of strategy helps the therapist to facilitate the neurorehabilitation remotely. Thus, the patients can stay in their homes, resulting in safer and less expensive costs. Meanwhile, several trainees in medical education centers can be trained by participating partially in the rehabilitation process. The trainees participate in a “hands-on” manner; so, they feel like they are rehabilitating the patient directly. For implementing such a scheme, a novel theoretical method is proposed using the power of multi-agent systems (MAS) theory into the multi-lateral teleoperation, based on the self-intelligence in the MAS. In the previous related works, changing the number of participants in the multi-lateral teleoperation tasks required redesigning the controllers; while, in this paper using both of the decentralized control and the self-intelligence of the MAS, avoids the need for redesigning the controller in the proposed structure. Moreover, in this research, uncertainties in the operators' dynamics, as well as time-varying delays in the communication channels, are taken into account. It is shown that the proposed structure has two tuning matrices (L and D) that can be used for different scenarios of multi-lateral teleoperation. By choosing proper tuning matrices, many related works about the multi-lateral teleoperation/telerehabilitation process can be implemented. In the final section of the paper, several scenarios were introduced to achieve “Simultaneous Training and Therapy” in TR and are implemented with the proposed structure. The results confirmed the stability and performance of the proposed framework.

It is well-established in the literature that biases (e. g., related to body size, ethnicity, race etc.) can occur during the employment interview and that applicants' fairness perceptions related to selection procedures can influence attitudes, intentions, and behaviors toward the recruiting organization. This study explores how social robotics may affect this situation. Using an online, video vignette-based experimental survey (n = 235), the study examines applicant fairness perceptions of two types of job interviews: a face-to-face and a robot-mediated interview. To reduce the risk of socially desirable responses, desensitize the topic, and detect any inconsistencies in the respondents' reactions to vignette scenarios, the study employs a first-person and a third-person perspective. In the robot-mediated interview, two teleoperated robots are used as fair proxies for the applicant and the interviewer, thus providing symmetrical visual anonymity unlike prior research that relied on asymmetrical anonymity, in which only one party was anonymized. This design is intended to eliminate visual cues that typically cause implicit biases and discrimination of applicants, but also to prevent biasing the interviewer's assessment through impression management tactics typically used by applicants. We hypothesize that fairness perception (i.e., procedural fairness and interactional fairness) and behavioral intentions (i.e., intentions of job acceptance, reapplication intentions, and recommendation intentions) will be higher in a robot-mediated job interview than in a face-to-face job interview, and that this effect will be stronger for introvert applicants. The study shows, contrary to our expectations, that the face-to-face interview is perceived as fairer, and that the applicant's personality (introvert vs. extravert) does not affect this perception. We discuss this finding and its implications, and address avenues for future research.

Traditionally, the robotic end-effectors that are employed in unstructured and dynamic environments are rigid and their operation requires sophisticated sensing elements and complicated control algorithms in order to handle and manipulate delicate and fragile objects. Over the last decade, considerable research effort has been put into the development of adaptive, under-actuated, soft robots that facilitate robust interactions with dynamic environments. In this paper, we present soft, retractable, pneumatically actuated, telescopic actuators that facilitate the efficient execution of stable grasps involving a plethora of everyday life objects. The efficiency of the proposed actuators is validated by employing them in two different soft and hybrid robotic grippers. The hybrid gripper uses three rigid fingers to accomplish the execution of all the tasks required by a traditional robotic gripper, while three inflatable, telescopic fingers provide soft interaction with objects. This synergistic combination of soft and rigid structures allows the gripper to cage/trap and firmly hold heavy and irregular objects. The second, simplistic and highly affordable robotic gripper employs just the telescopic actuators, exhibiting an adaptive behavior during the execution of stable grasps of fragile and delicate objects. The experiments demonstrate that both grippers can successfully and stably grasp a wide range of objects, being able to exert significantly high contact forces.

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.

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.

In nature, tip-localized growth allows navigation in tightly confined environments and creation of structures. Recently, this form of movement has been artificially realized through pressure-driven eversion of flexible, thin-walled tubes. Here we review recent work on robots that “grow” via pressure-driven eversion, referred to as “everting vine robots,” due to a movement pattern that is similar to that of natural vines. We break this work into four categories. First, we examine the design of everting vine robots, highlighting tradeoffs in material selection, actuation methods, and placement of sensors and tools. These tradeoffs have led to application-specific implementations. Second, we describe the state of and need for modeling everting vine robots. Quasi-static models of growth and retraction and kinematic and force-balance models of steering and environment interaction have been developed that use simplifying assumptions and limit the involved degrees of freedom. Third, we report on everting vine robot control and planning techniques that have been developed to move the robot tip to a target, using a variety of modalities to provide reference inputs to the robot. Fourth, we highlight the benefits and challenges of using this paradigm of movement for various applications. Everting vine robot applications to date include deploying and reconfiguring structures, navigating confined spaces, and applying forces on the environment. We conclude by identifying gaps in the state of the art and discussing opportunities for future research to advance everting vine robots and their usefulness in the field.

Gaze behavior is an important social signal between humans as it communicates locations of interest. People typically orient their attention to where others look as this informs about others' intentions and future actions. Studies have shown that humans can engage in similar gaze behavior with robots but presumably more so when they adopt the intentional stance toward them (i.e., believing robot behaviors are intentional). In laboratory settings, the phenomenon of attending toward the direction of others' gaze has been examined with the use of the gaze-cueing paradigm. While the gaze-cueing paradigm has been successful in investigating the relationship between adopting the intentional stance toward robots and attention orienting to gaze cues, it is unclear if the repetitiveness of the gaze-cueing paradigm influences adopting the intentional stance. Here, we examined if the duration of exposure to repetitive robot gaze behavior in a gaze-cueing task has a negative impact on subjective attribution of intentionality. Participants performed a short, medium, or long face-to-face gaze-cueing paradigm with an embodied robot while subjective ratings were collected pre and post the interaction. Results show that participants in the long exposure condition had the smallest change in their intention attribution scores, if any, while those in the short exposure condition had a positive change in their intention attribution, indicating that participants attributed more intention to the robot after short interactions. The results also show that attention orienting to robot gaze-cues was positively related to how much intention was attributed to the robot, but this relationship became more negative as the length of exposure increased. In contrast to subjective ratings, the gaze-cueing effects (GCEs) increased as a function of the duration of exposure to repetitive behavior. The data suggest a tradeoff between the desired number of trials needed for observing various mechanisms of social cognition, such as GCEs, and the likelihood of adopting the intentional stance toward a robot.

The history of humankind is full of examples that indicate a constant desire to make human beings more moral. Nowadays, technological breakthroughs might have a significant impact on our moral character and abilities. This is the case of Virtual Reality (VR) technologies. The aim of this paper is to consider the ethical aspects of the use of VR in enhancing empathy. First, we will offer an introduction to VR, explaining its fundamental features, devices and concepts. Then, we will approach the characterization of VR as an “empathy machine,” showing why this medium has aroused so much interest and why, nevertheless, we do not believe it is the ideal way to enhance empathy. As an alternative, we will consider fostering empathy-related abilities through virtual embodiment in avatars. In the conclusion, however, we will, we will examine some of the serious concerns related to the ethical relevance of empathy and will defend the philosophical case for a reason-guided empathy, also suggesting specific guidelines for possible future developments of empathy enhancement projects through VR embodied experiences.

The coronavirus disease 2019 (COVID-19) pandemic has resulted in public health interventions such as physical distancing restrictions to limit the spread and transmission of the novel coronavirus, causing significant effects on the delivery of physical healthcare procedures worldwide. The unprecedented pandemic spurs strong demand for intelligent robotic systems in healthcare. In particular, medical telerobotic systems can play a positive role in the provision of telemedicine to both COVID-19 and non-COVID-19 patients. Different from typical studies on medical teleoperation that consider problems such as time delay and information loss in long-distance communication, this survey addresses the consequences of physiological organ motion when using teleoperation systems to create physical distancing between clinicians and patients in the COVID-19 era. We focus on the control-theoretic approaches that have been developed to address inherent robot control issues associated with organ motion. The state-of-the-art telerobotic systems and their applications in COVID-19 healthcare delivery are reviewed, and possible future directions are outlined.

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] Robotica 2020 – November 10-14, 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 Bay Area Robotics Symposium – November 20, 2020 – [Online]

Let us know if you have suggestions for next week, and enjoy today's videos.

Snugglebot is what we all need right now.

[ Snugglebot ]

In his video message on his prayer intention for November, Pope Francis emphasizes that progress in robotics and artificial intelligence (AI) be oriented “towards respecting the dignity of the person and of Creation”.

[ Vatican News ]

KaPOW!

Apparently it's supposed to do that—the disruptor flies off backwards to reduce recoil on the robot, and has its own parachute to keep it from going too far.

[ Ghost Robotics ]

Animals have many muscles, receptors, and neurons which compose feedback loops. In this study, we designed artificial muscles, receptors, and neurons without any microprocessors, or software-based controllers. We imitate the reflexive rule observed in walking experiments of cats, as a result, the Pneumatic Brainless Robot II emerged running motion (a leg trajectory and a gait pattern) through the interaction between the body, the ground, and the artificial reflexes. We envision that the simple reflex circuit we discovered will be a candidate for a minimal model for describing the principles of animal locomotion.

Find the paper, "Brainless Running: A Quasi-quadruped Robot with Decentralized Spinal Reflexes by Solely Mechanical Devices," on IROS On-Demand.

[ IROS ]

Thanks Yoichi!

I have no idea what these guys are saying, but they're talking about robots that serve chocolate!

The world of experience of the Zotter Schokoladen Manufaktur of managing director Josef Zotter counts more than 270,000 visitors annually. Since March 2019, this world of chocolate in Bergl near Riegersburg in Austria has been enriched by a new attraction: the world's first chocolate and praline robot from KUKA delights young and old alike and serves up chocolate and pralines to guests according to their personal taste.

[ Zotter ]

This paper proposes a systematic solution that uses an unmanned aerial vehicle (UAV) to aggressively and safely track an agile target. The solution properly handles the challenging situations where the intent of the target and the dense environments are unknown to the UAV. The proposed solution is integrated into an onboard quadrotor system. We fully test the system in challenging real-world tracking missions. Moreover, benchmark comparisons validate that the proposed method surpasses the cutting-edge methods on time efficiency and tracking effectiveness.

[ FAST Lab ]

Southwest Research Institute developed a cable management system for collaborative robotics, or “cobots.” Dress packs used on cobots can create problems when cables are too tight (e-stops) or loose (tangling). SwRI developed ADDRESS, or the Adaptive DRESing System, to provide smarter cobot dress packs that address e-stops and tangling.

[ SWRI ]

A quick demonstration of the acoustic contact sensor in the RBO Hand 2. An embedded microphone records the sound inside of the pneumatic finger. Depending on which part of the finger makes contact, the sound is a little bit different. We create a sensor that recognizes these small changes and predicts the contact location from the sound. The visualization on the left shows the recorded sound (top) and which of the nine contact classes the sensor is currently predicting (bottom).

[ TU Berlin ]

The MAVLab won the prize for the “most innovative design” in the IMAV 2018 indoor competition, in which drones had to fly through windows, gates, and follow a predetermined flight path. The prize was awarded for the demonstration of a fully autonomous version of the “DelFly Nimble”, a tailless flapping wing drone.

In order to fly by itself, the DelFly Nimble was equipped with a single, small camera and a small processor allowing onboard vision processing and control. The jury of international experts in the field praised the agility and autonomous flight capabilities of the DelFly Nimble.

[ MAVLab ]

A reactive walking controller for the Open Dynamic Robot Initiative's skinny quadruped.

[ ODRI ]

Mobile service robots are already able to recognize people and objects while navigating autonomously through their operating environments. But what is the ideal position of the robot to interact with a user? To solve this problem, Fraunhofer IPA developed an approach that connects navigation, 3D environment modeling, and person detection to find the optimal goal pose for HRI.

[ Fraunhofer ]

Yaskawa has been in robotics for a very, very long time.

[ Yaskawa ]

Black in Robotics IROS launch event, featuring Carlotta Berry.

[ Black in Robotics ]

What is AI? I have no idea! But these folks have some opinions.

[ MIT ]

Aerial-based Observations of Volcanic Emissions (ABOVE) is an international collaborative project that is changing the way we sample volcanic gas emissions. Harnessing recent advances in drone technology, unoccupied aerial systems (UAS) in the ABOVE fleet are able to acquire aerial measurements of volcanic gases directly from within previously inaccessible volcanic plumes. In May 2019, a team of 30 researchers undertook an ambitious field deployment to two volcanoes – Tavurvur (Rabaul) and Manam in Papua New Guinea – both amongst the most prodigious emitters of sulphur dioxide on Earth, and yet lacking any measurements of how much carbon they emit to the atmosphere.

[ ABOVE ]

A talk from IHMC's Robert Griffin for ICCAS 2020, including a few updates on their Nadia humanoid.

[ IHMC ]

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] Robotica 2020 – November 10-14, 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 Bay Area Robotics Symposium – November 20, 2020 – [Online]

Let us know if you have suggestions for next week, and enjoy today's videos.

Snugglebot is what we all need right now.

[ Snugglebot ]

In his video message on his prayer intention for November, Pope Francis emphasizes that progress in robotics and artificial intelligence (AI) be oriented “towards respecting the dignity of the person and of Creation”.

[ Vatican News ]

KaPOW!

Apparently it's supposed to do that—the disruptor flies off backwards to reduce recoil on the robot, and has its own parachute to keep it from going too far.

[ Ghost Robotics ]

Animals have many muscles, receptors, and neurons which compose feedback loops. In this study, we designed artificial muscles, receptors, and neurons without any microprocessors, or software-based controllers. We imitate the reflexive rule observed in walking experiments of cats, as a result, the Pneumatic Brainless Robot II emerged running motion (a leg trajectory and a gait pattern) through the interaction between the body, the ground, and the artificial reflexes. We envision that the simple reflex circuit we discovered will be a candidate for a minimal model for describing the principles of animal locomotion.

Find the paper, "Brainless Running: A Quasi-quadruped Robot with Decentralized Spinal Reflexes by Solely Mechanical Devices," on IROS On-Demand.

[ IROS ]

Thanks Yoichi!

I have no idea what these guys are saying, but they're talking about robots that serve chocolate!

The world of experience of the Zotter Schokoladen Manufaktur of managing director Josef Zotter counts more than 270,000 visitors annually. Since March 2019, this world of chocolate in Bergl near Riegersburg in Austria has been enriched by a new attraction: the world's first chocolate and praline robot from KUKA delights young and old alike and serves up chocolate and pralines to guests according to their personal taste.

[ Zotter ]

This paper proposes a systematic solution that uses an unmanned aerial vehicle (UAV) to aggressively and safely track an agile target. The solution properly handles the challenging situations where the intent of the target and the dense environments are unknown to the UAV. The proposed solution is integrated into an onboard quadrotor system. We fully test the system in challenging real-world tracking missions. Moreover, benchmark comparisons validate that the proposed method surpasses the cutting-edge methods on time efficiency and tracking effectiveness.

[ FAST Lab ]

Southwest Research Institute developed a cable management system for collaborative robotics, or “cobots.” Dress packs used on cobots can create problems when cables are too tight (e-stops) or loose (tangling). SwRI developed ADDRESS, or the Adaptive DRESing System, to provide smarter cobot dress packs that address e-stops and tangling.

[ SWRI ]

A quick demonstration of the acoustic contact sensor in the RBO Hand 2. An embedded microphone records the sound inside of the pneumatic finger. Depending on which part of the finger makes contact, the sound is a little bit different. We create a sensor that recognizes these small changes and predicts the contact location from the sound. The visualization on the left shows the recorded sound (top) and which of the nine contact classes the sensor is currently predicting (bottom).

[ TU Berlin ]

The MAVLab won the prize for the “most innovative design” in the IMAV 2018 indoor competition, in which drones had to fly through windows, gates, and follow a predetermined flight path. The prize was awarded for the demonstration of a fully autonomous version of the “DelFly Nimble”, a tailless flapping wing drone.

In order to fly by itself, the DelFly Nimble was equipped with a single, small camera and a small processor allowing onboard vision processing and control. The jury of international experts in the field praised the agility and autonomous flight capabilities of the DelFly Nimble.

[ MAVLab ]

A reactive walking controller for the Open Dynamic Robot Initiative's skinny quadruped.

[ ODRI ]

Mobile service robots are already able to recognize people and objects while navigating autonomously through their operating environments. But what is the ideal position of the robot to interact with a user? To solve this problem, Fraunhofer IPA developed an approach that connects navigation, 3D environment modeling, and person detection to find the optimal goal pose for HRI.

[ Fraunhofer ]

Yaskawa has been in robotics for a very, very long time.

[ Yaskawa ]

Black in Robotics IROS launch event, featuring Carlotta Berry.

[ Black in Robotics ]

What is AI? I have no idea! But these folks have some opinions.

[ MIT ]

Aerial-based Observations of Volcanic Emissions (ABOVE) is an international collaborative project that is changing the way we sample volcanic gas emissions. Harnessing recent advances in drone technology, unoccupied aerial systems (UAS) in the ABOVE fleet are able to acquire aerial measurements of volcanic gases directly from within previously inaccessible volcanic plumes. In May 2019, a team of 30 researchers undertook an ambitious field deployment to two volcanoes – Tavurvur (Rabaul) and Manam in Papua New Guinea – both amongst the most prodigious emitters of sulphur dioxide on Earth, and yet lacking any measurements of how much carbon they emit to the atmosphere.

[ ABOVE ]

A talk from IHMC's Robert Griffin for ICCAS 2020, including a few updates on their Nadia humanoid.

[ IHMC ]

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.”
 

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.”
 

Digital Nature Group at the University of Tsukuba in Japan is working towards a “post ubiquitous computing era consisting of seamless combination of computational resources and non-computational resources.” By “non-computational resources,” they mean leveraging the natural world, which for better or worse includes insects

At small scales, the capabilities of insects far exceed the capabilities of robots. I get that. And I get that turning cockroaches into an army of insect cyborgs could be useful in a variety of ways. But what makes me fundamentally uncomfortable is the idea that “in the future, they’ll appear out of nowhere without us recognizing it, fulfilling their tasks and then hiding.” In other words, you’ll have cyborg cockroaches hiding all over your house, all the time.

Warning: This article contains video of cockroaches being modified with cybernetic implants that some people may find upsetting.

Remote controlling cockroaches isn’t a new idea, and it’s a fairly simple one. By stimulating the left or right antenna nerves of the cockroach, you can make it think that it’s running into something, and get it to turn in the opposite direction. Add wireless connectivity, some fiducial markers, an overhead camera system, and a bunch of cyborg cockroaches, and you have a resilient swarm that can collaborate on tasks. The researchers suggest that the swarm could be used as a display (by making each cockroach into a pixel), to transport objects, or to draw things. There’s also some mention of “input or haptic interfaces or an audio device,” which frankly sounds horrible.

The reason to use cockroaches is that you can take advantage of their impressive ruggedness, efficiency, high power to weight ratio, and mobility. They can also feed themselves, meaning that whenever you don’t need the swarm to perform some task for you, you can deactivate the control system and let them scurry off to find crumbs in dark places. 

There are many other swarm robotic platforms that can perform what you’re seeing these cyborg roaches do, but according to the researchers, the reason to use cockroaches is that you can take advantage of their impressive ruggedness, efficiency, high power to weight ratio, and mobility. They’re a lot messier (yay biology!), but they can also feed themselves, meaning that whenever you don’t need the swarm to perform some task for you, you can deactivate the control system and let them scurry off to find crumbs in dark places. And when you need them again, turn the control system on and experience the nightmare of your cyborg cockroach swarm reassembling itself from all over your house. 

While we’re on the subject of cockroach hacking, we would be doing you a disservice if we didn’t share some of project leader Yuga Tsukuda’s other projects. Here’s a cockroach-powered clock, about which the researchers note that “it is difficult to control the cockroaches when trying to control them by electrical stimulation because they move spontaneously. However, by cutting off the head and removing the brain, they do not move spontaneously and the control by the computer becomes easy.” So, zombie cockroaches. Good then.

And if that’s not enough for you, how about this:

The researchers describe this project as an “attempt to use cockroaches for makeup by sticking them on the face.” They stick electrodes into the cockroaches to make them wiggle their legs when electrical stimulation is applied. And the peacock feathers? They “make the cockroach movement bigger, and create a cosmic mystery.”

Digital Nature Group at the University of Tsukuba in Japan is working towards a “post ubiquitous computing era consisting of seamless combination of computational resources and non-computational resources.” By “non-computational resources,” they mean leveraging the natural world, which for better or worse includes insects

At small scales, the capabilities of insects far exceed the capabilities of robots. I get that. And I get that turning cockroaches into an army of insect cyborgs could be useful in a variety of ways. But what makes me fundamentally uncomfortable is the idea that “in the future, they’ll appear out of nowhere without us recognizing it, fulfilling their tasks and then hiding.” In other words, you’ll have cyborg cockroaches hiding all over your house, all the time.

Warning: This article contains video of cockroaches being modified with cybernetic implants that some people may find upsetting.

Remote controlling cockroaches isn’t a new idea, and it’s a fairly simple one. By stimulating the left or right antenna nerves of the cockroach, you can make it think that it’s running into something, and get it to turn in the opposite direction. Add wireless connectivity, some fiducial markers, an overhead camera system, and a bunch of cyborg cockroaches, and you have a resilient swarm that can collaborate on tasks. The researchers suggest that the swarm could be used as a display (by making each cockroach into a pixel), to transport objects, or to draw things. There’s also some mention of “input or haptic interfaces or an audio device,” which frankly sounds horrible.

The reason to use cockroaches is that you can take advantage of their impressive ruggedness, efficiency, high power to weight ratio, and mobility. They can also feed themselves, meaning that whenever you don’t need the swarm to perform some task for you, you can deactivate the control system and let them scurry off to find crumbs in dark places. 

There are many other swarm robotic platforms that can perform what you’re seeing these cyborg roaches do, but according to the researchers, the reason to use cockroaches is that you can take advantage of their impressive ruggedness, efficiency, high power to weight ratio, and mobility. They’re a lot messier (yay biology!), but they can also feed themselves, meaning that whenever you don’t need the swarm to perform some task for you, you can deactivate the control system and let them scurry off to find crumbs in dark places. And when you need them again, turn the control system on and experience the nightmare of your cyborg cockroach swarm reassembling itself from all over your house. 

While we’re on the subject of cockroach hacking, we would be doing you a disservice if we didn’t share some of project leader Yuga Tsukuda’s other projects. Here’s a cockroach-powered clock, about which the researchers note that “it is difficult to control the cockroaches when trying to control them by electrical stimulation because they move spontaneously. However, by cutting off the head and removing the brain, they do not move spontaneously and the control by the computer becomes easy.” So, zombie cockroaches. Good then.

And if that’s not enough for you, how about this:

The researchers describe this project as an “attempt to use cockroaches for makeup by sticking them on the face.” They stick electrodes into the cockroaches to make them wiggle their legs when electrical stimulation is applied. And the peacock feathers? They “make the cockroach movement bigger, and create a cosmic mystery.”

Ensuring care is one of the biggest humanitarian challenges of the future since an acute shortage in nursing staff is expected. At the same time, this offers the opportunity for new technologies in nursing, as the use of robotic systems. One potential use case is outpatient care, which nowadays involves traveling long distances. Here, the use of telerobotics could provide a major relief for the nursing staff, as it could spare them many of those—partially far—journeys. Since autonomous robotic systems are not desired at least in Germany for ethical reasons, this paper evaluates the design of a telemanipulation system consisting of off-the-shelf components for outpatient care. Furthermore, we investigated the suitability of two different input devices for control, a kinesthetic device, and a keyboard plus mouse. We conducted the investigations in a laboratory study. This laboratory represents a realistic environment of an elderly home and a remote care service center. It was carried out with 25 nurses. Tasks common in outpatient care, such as handing out things (manipulation) and examining body parts (set camera view), were used in the study. After a short training period, all nurses were able to control a manipulator with the two input devices and perform the two tasks. It was shown that the Falcon leads to shorter execution times (on average 0:54.82 min, compared to 01:10.92 min with keyboard and mouse), whereby the participants were more successful with the keyboard plus mouse, in terms of task completion. There is no difference in usability and cognitive load. Moreover, we pointed out, that the access to this kind of technology is desirable, which is why we identified further usage scenarios.

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.aitweeted 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

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.aitweeted 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

Pages