Teaching collaborative multi-robot tasks through demonstration

Humanoid robots working alongside humans in everyday environments is a long standing goal of the robotics community. To achieve this goal, methods for developing new robot behaviors that are intuitive and accessible to non-programmers are required. In this paper, we present a demonstration-based method for teaching distributed autonomous robots to coordinate their actions and perform collaborative multi-robot tasks. Within the presented framework, each robot learns an individual policy from teacher demonstrations using a confidence-based algorithm. Based on this learning approach, we contribute three techniques for teaching multi-robot coordination using different information sharing strategies. We evaluate and compare these approaches by teaching two Sony QRIO humanoid robots to perform three collaborative ball sorting tasks.

[1]  Paul E. Utgoff,et al.  On integrating apprentice learning and reinforcement learning , 1996 .

[2]  C. Boutilier,et al.  Accelerating Reinforcement Learning through Implicit Imitation , 2003, J. Artif. Intell. Res..

[3]  Gordon Cheng,et al.  Learning to Act from Observation and Practice , 2004, Int. J. Humanoid Robotics.

[4]  Manuela M. Veloso,et al.  Teaching multi-robot coordination using demonstration of communication and state sharing , 2008, AAMAS.

[5]  Manuela M. Veloso,et al.  Interactive Policy Learning through Confidence-Based Autonomy , 2014, J. Artif. Intell. Res..

[6]  Masayuki Inaba,et al.  Learning by watching: extracting reusable task knowledge from visual observation of human performance , 1994, IEEE Trans. Robotics Autom..

[7]  Luís Nunes,et al.  On Learning by Exchanging Advice , 2002, ArXiv.

[8]  Aude Billard,et al.  Incremental learning of gestures by imitation in a humanoid robot , 2007, 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[9]  Maja J. Mataric,et al.  Exemplar-based primitives for humanoid movement classification and control , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[10]  Sascha Ossowski,et al.  On coordination and its significance to distributed and multi-agent systems: Research Articles , 2006 .

[11]  Maja J. Matarić,et al.  Principled Approaches to the Design of Multi-Robot Systems , 2004 .

[12]  T. Ishida Development of a small biped entertainment robot QRIO , 2004, Micro-Nanomechatronics and Human Science, 2004 and The Fourth Symposium Micro-Nanomechatronics for Information-Based Society, 2004..

[13]  Maja J. Matarić,et al.  Primitive-Based Movement Classification for Humanoid Imitation , 2000 .

[14]  Sascha Ossowski,et al.  On coordination and its significance to distributed and multi‐agent systems , 2006, Concurr. Comput. Pract. Exp..

[15]  Andrea Lockerd Thomaz,et al.  Tutelage and socially guided robot learning , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[16]  Manuela M. Veloso,et al.  Confidence-based policy learning from demonstration using Gaussian mixture models , 2007, AAMAS '07.