A framework integrating statistical and social cues to teach a humanoid robot new skills

Bringing robots as collaborative partners into homes presents various challenges to human-robot interaction. Robots will need to interact with untrained users in environments that are originally designed for humans. Compared to their industrial homologous form, humanoid robots can not be preprogrammed with an initial set of behaviours. They should adapt their skills to a huge range of possible tasks without needing to change the environments and tools to fit their needs. The rise of these humanoids implies an inherent social dimension to this technology, where the end-users should be able to teach new skills to these robots in an intuitive manner, relying only on their experience in teaching new skills to other human partners. Our research aims at designing a generic Robot Programming by Demonstration (RPD) framework based on a probabilistic representation of the task constraints, which allows to integrate information from cross-situational statistics and from various social cues such as joint attention or vocal intonation. This paper presents our ongoing research towards bringing user- friendly human-robot teaching systems that would speed up the skill transfer process.

[1]  Michael I. Jordan,et al.  Supervised learning from incomplete data via an EM approach , 1993, NIPS.

[2]  Aude Billard,et al.  Reinforcement learning for imitating constrained reaching movements , 2007, Adv. Robotics.

[3]  Aude Billard,et al.  What is the Teacher"s Role in Robot Programming by Demonstration? - Toward Benchmarks for Improved Learning , 2007 .

[4]  Aude Billard,et al.  Combining Dynamical Systems control and programming by demonstration for teaching discrete bimanual coordination tasks to a humanoid robot , 2008, 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[5]  Chen Yu,et al.  A unified model of early word learning: Integrating statistical and social cues , 2007, Neurocomputing.

[6]  Stefan Schaal,et al.  Incremental Online Learning in High Dimensions , 2005, Neural Computation.

[7]  Rüdiger Dillmann,et al.  Incremental Learning of Tasks From User Demonstrations, Past Experiences, and Vocal Comments , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Andrea Lockerd Thomaz,et al.  Tutelage and Collaboration for Humanoid Robots , 2004, Int. J. Humanoid Robotics.

[9]  Gordon Cheng,et al.  Coaching: An Approach to Efficiently and Intuitively Create Humanoid Robot Behaviors , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[10]  G. Csibra,et al.  The social construction of the cultural mind: Imitative learning as a mechanism of human pedagogy , 2005 .

[11]  C. Breazeal,et al.  Transparency and Socially Guided Machine Learning , 2006 .

[12]  Henrik I. Christensen,et al.  Situated Dialogue and Spatial Organization: What, Where… and Why? , 2007 .

[13]  Yoshihiko Nakamura,et al.  Acquiring Motion Elements for Bidirectional Computation of Motion Recognition and Generation , 2002, ISER.

[14]  Aude Billard,et al.  Teaching a Humanoid Robot to Recognize and Reproduce Social Cues , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.

[15]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[16]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[17]  Aude Billard,et al.  Learning Dynamical System Modulation for Constrained Reaching Tasks , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

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

[19]  Aude Billard,et al.  On Learning, Representing, and Generalizing a Task in a Humanoid Robot , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Chrystopher L. Nehaniv,et al.  Teaching robots by moulding behavior and scaffolding the environment , 2006, HRI '06.

[21]  Kerstin Dautenhahn,et al.  Of hummingbirds and helicopters: An algebraic framework for interdisciplinary studies of imitation a , 2000 .

[22]  Britta Wrede,et al.  Playing a different imitation game: Interaction with an Empathic Android Robot , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[23]  Yuichiro Yoshikawa,et al.  Responsive Robot Gaze to Interaction Partner , 2006, Robotics: Science and Systems.

[24]  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).

[25]  Jun Tani,et al.  Joint attention between a humanoid robot and users in imitation game , 2004 .

[26]  Peter Ford Dominey,et al.  Robot command, interrogation and teaching via social interaction , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[27]  Xuejing Sun,et al.  Pitch determination and voice quality analysis using Subharmonic-to-Harmonic Ratio , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[28]  Marc Hanheide,et al.  Human-Oriented Interaction With an Anthropomorphic Robot , 2007, IEEE Transactions on Robotics.

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

[30]  Sylvain Calinon,et al.  Continuous extraction of task constraints in a robot programming by demonstration framework , 2007 .