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...

Before beginning any robot task, users must position the robot's base, a task that now depends entirely on user intuition. While slight perturbation is tolerable for robots with moveable bases, correcting the problem is imperative for fixedbase robots if some essential task sections are out of reach. For mobile manipulation robots, it is necessary to decide on a specific base position before beginning manipulation tasks. This paper presents Reuleaux, an open source library for robot reachability analyses and base placement. It reduces the amount of extra repositioning and removes the manual work of identifying potential base locations. Based on the reachability map, base placement locations of a whole robot or only the arm can be efficiently determined. This can be applied to both statically mounted robots, where position of the robot and work piece ensure the maximum amount of work performed, and to mobile robots, where the maximum amount of workable area can be reached. Solutions are not limited only to vertically constrained placement, since complicated robotics tasks require the base to be placed at unique poses based on task demand.All Reuleaux library methods were tested on different robots of different specifications and evaluated for tasks in simulation and real world environment. Evaluation results indicate that Reuleaux had significantly improved performance than prior existing methods in terms of time-efficiency and range of applicability.

With the goal of advancing the state of automatic robotic grasping, we present a novel approach that combines machine learning techniques and rigorous validation on a physical robotic platform in order to develop an algorithm that predicts the quality of a robotic grasp before execution. After collecting a large grasp sample set (522 grasps), we first conduct a thorough statistical analysis of the ability of grasp metrics that are commonly used in the robotics literature to discriminate between good and bad grasps. We then apply Principal Component Analysis and Gaussian Process algorithms on the discriminative grasp metrics to build a classifier that predicts grasp quality. The key findings are as follows: (i) several of the grasp metrics in the literature are weak predictors of grasp quality when implemented on a physical robotic platform; (ii) the Gaussian Process-based classifier significantly improves grasp prediction techniques by providing an absolute grasp quality prediction score from combining multiple grasp metrics. Specifically, the GP classifier showed a 66% percent improvement in the True Positive classification rate at a low False Positive rate of 5% when compared with classification based on thresholding of individual grasp metrics.978-1-4799-6934-0/14/$31.00 ©2014 IEEE

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