Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies

We propose a control strategy for a robotic manipulator operating in an unstructured environment while interacting with a human operator. The proposed system takes into account the important characteristics of the task and the redundancy of the robot to determine a controller that is safe for the user. The constraints of the task are first extracted using several examples of the skill demonstrated to the robot through kinesthetic teaching. An active control strategy based on task-space control with variable stiffness is proposed, and combined with a safety strategy for tasks requiring humans to move in the vicinity of robots. A risk indicator for human-robot collision is defined, which modulates a repulsive force distorting the spatial and temporal characteristics of the movement according to the task constraints. We illustrate the approach with two human-robot interaction experiments, where the user teaches the robot first how to move a tray, and then shows it how to iron a napkin.

[1]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[2]  J. P. Vautrin,et al.  Manipulating industrial robots in France — Effects on health, safety and working conditions: Results of the INRS—CRAM survey , 1986 .

[3]  Exploiting redundancy to reduce impact force , 1991, Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91.

[4]  S. P. Gaskill,et al.  Safety issues in modern applications of robots , 1994 .

[5]  Pradeep K. Khosla,et al.  Exploiting redundancy to reduce impact force , 1994, J. Intell. Robotic Syst..

[6]  Jun Nakanishi,et al.  Trajectory formation for imitation with nonlinear dynamical systems , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[7]  Antonio Bicchi,et al.  Fast and "soft-arm" tactics [robot arm design] , 2004, IEEE Robotics & Automation Magazine.

[8]  Alessandro De Luca,et al.  Collision Detection and Safe Reaction with the DLR-III Lightweight Manipulator Arm , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Stefan Schaal,et al.  Dynamics systems vs. optimal control--a unifying view. , 2007, Progress in brain research.

[10]  Alessandro De Luca,et al.  Exploiting robot redundancy in collision detection and reaction , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Aude Billard,et al.  A framework integrating statistical and social cues to teach a humanoid robot new skills , 2008, ICRA 2008.

[12]  Stefan Schaal,et al.  Robot Programming by Demonstration , 2009, Springer Handbook of Robotics.

[13]  Darwin G. Caldwell,et al.  Handling of multiple constraints and motion alternatives in a robot programming by demonstration framework , 2009, 2009 9th IEEE-RAS International Conference on Humanoid Robots.

[14]  Nikolaos G. Tsagarakis,et al.  Safe human robot interaction via energy regulation control , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Aaron Hertzmann,et al.  Prioritized optimization for task-space control , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[17]  Alin Albu-Schäffer,et al.  Requirements for Safe Robots: Measurements, Analysis and New Insights , 2009, Int. J. Robotics Res..

[18]  Stefan Schaal,et al.  Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance , 2009, 2009 IEEE International Conference on Robotics and Automation.

[19]  Antonio Bicchi,et al.  Integration of active and passive compliance control for safe human-robot coexistence , 2009, 2009 IEEE International Conference on Robotics and Automation.

[20]  Sethu Vijayakumar,et al.  Methods for Learning Control Policies from Variable-Constraint Demonstrations , 2010, From Motor Learning to Interaction Learning in Robots.

[21]  Aude Billard,et al.  BM: An iterative algorithm to learn stable non-linear dynamical systems with Gaussian mixture models , 2010, 2010 IEEE International Conference on Robotics and Automation.

[22]  Darwin G. Caldwell,et al.  Learning and Reproduction of Gestures by Imitation , 2010, IEEE Robotics & Automation Magazine.

[23]  Eric L. Sauser,et al.  An Approach Based on Hidden Markov Model and Gaussian Mixture Regression , 2010 .