Email: tobias at gatech.edu
Office: College of Computing Building, Room 270
Humanoid Robotics Lab
Center for Robotics and Intelligent Machines
College of Computing
Georgia Institute of Technology
We introduce and experimentally validate a novel algorithmic model for physical human-robot interaction with hybrid dynamics. Our computational solutions are complementary to passive and compliant hardware. We focus on the case where human motion can be predicted. In these cases, the robot can select optimal motions in response to human actions and maximize safety. By representing the domain as a Markov Game, we enable the robot to not only react to the human but also to construct an infinite horizon optimal policy of actions and responses. Experimentally, we apply our model to simulated robot sword defense. Our approach enables a simulated 7-DOF robot arm to block known attacks in any sequence. We generate optimized blocks and apply game theoretic tools to choose the best action for the defender in the presence of an intelligent adversary.
T. Kunz, P. Kingston, M. Stilman, M. Egerstedt. Dynamic Chess: Strategic Planning for Robot Motion. IEEE International Conference on Robotics and Automation, 2011. [pdf] [video]
We present a practical strategy for real-time path planning for articulated robot arms in changing environments by integrating PRM for Changing Environments with 3D sensor data. Our implementation on Care-O-Bot 3 identifies bottlenecks in the algorithm and introduces new methods that solve the overall task of detecting obstacles and planning a path around them in under 100 ms.
A fast planner is necessary to enable the robot to react to quickly changing human environments. We have tested our implementation in real-world experiments where a human subject enters the manipulation area, is detected and safely avoided by the robot. This capability is critical for future applications in automation and service robotics where humans will work closely with robots to jointly perform tasks.
T. Kunz, U. Reiser, M. Stilman, A. Verl. Real-Time Path Planning for a Robot Arm in Changing Environments. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010, Taipei, Taiwan. [pdf]
T. Kunz. Real-Time Motion Planning for a Robot Arm in Dynamic Environments. Diplom thesis, University of Stuttgart, 2009. [pdf]
We present successful control strategies for dynamically stable robots that avoid low ceilings and other vertical obstacles in a manner similar to limbo dances. Given the parameters of the mission, including the goal and obstacle dimensions, our method uses a sequential composition of IO-linearized controllers and applies stochastic optimization to automatically compute the best controller gains and references, as well as the times for switching between the different controllers. We demonstrate this system through numerical simulations, validation in a physics-based simulation environment, as well as on a novel two-wheeled platform. The results show that the generated control strategies are successful in mission planning for this challenging problem domain and offer significant advantages over hand-tuned alternatives.
K. Teeyapan, J. Wang, T. Kunz, and M. Stilman. Robot Limbo: Optimized planning and control for dynamically stable robots under vertical obstacles. IEEE International Conference on Robotics and Automation, 2010. [pdf]