Robotics: Science and Systems II

Dynamic Imitation in a Humanoid Robot through Nonparametric Probabilistic Inference

D. Grimes, R. Chalodhorn, R. Rao

Abstract: We tackle the problem of learning imitative wholebody motions in a humanoid robot using probabilistic inference in Bayesian networks. Our approach exploits the rich prior information obtained from human motion capture data for dynamic imitation. Dynamic imitation implies that the robot must interact with its environment and account for forces such as gravity and inertia during imitation. Rather than explicitly modeling these forces and the body of the humanoid as in traditional approaches, we show that imitative motion can be achieved by learning a sensor-based representation of dynamic balance. Bayesian networks provide a sound theoretical framework for combining both the kinematic prior information from observing a human demonstrator and the dynamic prior information which, with high probability will keep the motion dynamically balanced during imitation. By posing the problem as one of inference in a Bayesian network, we show that methods developed for approximate inference can be leveraged to efficiently perform inference of actions. Additionally, by using nonparametric inference and a nonparametric (Gaussian process) forward model, our approach does not make any strong assumptions about the physical environment or the mass and inertial properties of the humanoid robot. We propose an iterative, probabilistically constrained algorithm for exploring the space of motor commands and show that the algorithm can quickly discover dynamically stable actions for whole-body imitation of human motion. Experimental results based on simulation and subsequent execution by a real Hoap-2 humanoid robot demonstrate that our algorithm is able to imitate a human performing actions such as squatting and a one-legged balance.



    AUTHOR    = {D. Grimes and R. Chalodhorn and R. Rao},
    TITLE     = {Dynamic Imitation in a Humanoid Robot through Nonparametric Probabilistic Inference},
    BOOKTITLE = {Proceedings of Robotics: Science and Systems},
    YEAR      = {2006},
    ADDRESS   = {Philadelphia, USA},
    MONTH     = {August},
    DOI       = {10.15607/RSS.2006.II.026}