Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input

A method to learn and reproduce robot force interactions in a human–robot interaction setting is proposed. The method allows a robotic manipulator to learn to perform tasks that require exerting forces on external objects by interacting with a human operator in an unstructured environment. This is achieved by learning two aspects of a task: positional and force profiles. The positional profile is obtained from task demonstrations via kinesthetic teaching. The force profile is obtained from additional demonstrations via a haptic device. A human teacher uses the haptic device to input the desired forces that the robot should exert on external objects during the task execution. The two profiles are encoded as a mixture of dynamical systems, which is used to reproduce the task satisfying both the positional and force profiles. An active control strategy based on task-space control with variable stiffness is then proposed to reproduce the skill. The method is demonstrated with two experiments in which the robot learns an ironing task and a door-opening task.

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

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

[3]  Thomas G. Dietterich.  Adaptive computation and machine learning , 1998 .

[4]  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).

[5]  N. Hodges,et al.  Modelling coaching practice: the role of instruction and demonstration , 2002, Journal of sports sciences.

[6]  Jian S. Dai,et al.  Trajectory and orientation analysis of the ironing process for robotic automation , 2004 .

[7]  R. R. Horn,et al.  Observational learning : Is it time we took another look? , 2004 .

[8]  Jian S. Dai,et al.  Folding algorithms and mechanisms synthesis for robotic ironing , 2004 .

[9]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[10]  Andrea Lockerd Thomaz,et al.  An embodied computational model of social referencing , 2005, ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005..

[11]  Katharina J. Rohlfing,et al.  How can multimodal cues from child-directed interaction reduce learning complexity in robots? , 2006, Adv. Robotics.

[12]  I. René J. A. te Boekhorst,et al.  Learning about natural human-robot interaction styles , 2006, Robotics Auton. Syst..

[13]  Alin Albu-Schäffer,et al.  The DLR lightweight robot: design and control concepts for robots in human environments , 2007, Ind. Robot.

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

[15]  Antonio Bicchi,et al.  An atlas of physical human-robot interaction , 2008 .

[16]  Chrystopher L. Nehaniv,et al.  Teaching robot companions: the role of scaffolding and event structuring , 2008, Connect. Sci..

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

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

[19]  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.

[20]  Aude Billard,et al.  Statistical Learning by Imitation of Competing Constraints in Joint Space and Task Space , 2009, Adv. Robotics.

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

[22]  Paul Evrard,et al.  Teaching physical collaborative tasks: object-lifting case study with a humanoid , 2009, 2009 9th IEEE-RAS International Conference on Humanoid Robots.

[23]  Henk Nijmeijer,et al.  Robot Programming by Demonstration , 2010, SIMPAR.

[24]  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.

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

[26]  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.

[27]  Darwin G. Caldwell,et al.  Robot motor skill coordination with EM-based Reinforcement Learning , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Darwin G. Caldwell,et al.  Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

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

[31]  Pieter Abbeel,et al.  Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding , 2010, 2010 IEEE International Conference on Robotics and Automation.