Team ViGIR entered the 2013 DARPA Robotics Challenge (DRC) with a focus on developing software to enable an operator to guide a humanoid robot through the series of challenge tasks emulating disaster response scenarios. The overarching philosophy was to make our operators full team members and not just mere supervisors. We designed our operator control station (OCS) to allow multiple operators to request and share information as needed to maintain situational awareness under bandwidth constraints, while directing the robot to perform tasks with most planning and control taking place onboard the robot. Given the limited development time, we leveraged a number of open source libraries in both our onboard software and our OCS design; this included significant use of the robot operating system libraries and toolchain. This paper describes the high level approach, including the OCS design and major onboard components, and it presents our DRC Trials results. The paper concludes with a number of lessons learned that are being applied to the final phase of the competition and are useful for related projects as well. C 2014 Wiley Periodicals, Inc.
Kohlbrecher et al.: Human-Robot Teaming for Rescue Missions • 353independence (Huang et al., 2007). The human members of the team function as supervisors who set high-level goals, teammates who assist the robot with perception tasks, and operators who directly change robot parameters to improve performance (Scholtz, 2003); as these roles change dynamically during a set task in our system, we will use the term operator generically. Following Bruemmer et al. (2002), we rarely operate in teleoperation where we directly control a joint value, and we primarily operate in shared mode where the operator specifies tasks or goal points. In shared mode, the robot plans its motions to avoid obstacles and then executes the motion only when given permission. Even when executing a footstep plan in autonomous mode, the operator still has supervisory control of the robot and can command the robot to stop walking at any time and safely revert to a standing posture.Team ViGIR entered the DRC as a "Track B" team competing in the DARPA Virtual Robotics Challenge (VRC). Initially, Team ViGIR was composed of TORC Robotics, 2 the Simulation, Systems Optimization, and Robotics Group at Technische Universität Darmstadt (TUD), 3 and the 3D Interaction Group at Virginia Tech. 4 With only eight months from program kickoff to the first competition, the team focused on providing basic robot capabilities needed for the three tasks in the VRC. A short overview of our VRC approach is available in Kohlbrecher et al. (2013).While the tasks and requirements for the VRC were based on those anticipated in a real scenario, there were important differences: sensor noise was low and more predictable, simple friction models were used, there was no need for calibrating sensors or joint angle offsets for the robot, and the environments were known ahead of time. The dynamic model used for simulating the Atlas robot was ava...
Team ViGIR and Team Hector participated in the DARPA Robotics Challenge (DRC) Finals, held June 2015 in Pomona, California, along with 21 other teams from around the world. Both teams competed using the same high‐level software, in conjunction with independently developed low‐level software specific to their humanoid robots. On the basis of previous work on operator‐centric manipulation control at the level of affordances, we developed an approach that allows one or more human operators to share control authority with a high‐level behavior controller. This collaborative autonomy decreases the completion time of manipulation tasks, increases the reliability of the human‐robot team, and allows the operators to adjust the robotic system's autonomy on‐the‐fly. This article discusses the technical challenges we faced and overcame during our efforts to allow the human operators to interact with the robotic system at a higher level of abstraction and share control authority with it. We introduce and evaluate the proposed approach in the context of our two teams' participation in the DRC Finals. We also present additional, systematic experiments conducted in the lab afterward. Finally, we present a discussion about the lessons learned while transitioning between operator‐centered manipulation control and behavior‐centered manipulation control during competition.
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