Learning to Walk through Imitation

Programming a humanoid robot to walk is a challenging problem in robotics. Traditional approaches rely heavily on prior knowledge of the robot's physical parameters to devise sophisticated control algorithms for generating a stable gait. In this paper, we provide, to our knowledge, the first demonstration that a humanoid robot can learn to walk directly by imitating a human gait obtained from motion capture (mocap) data. Training using human motion capture is an intuitive and flexible approach to programming a robot but direct usage of mocap data usually results in dynamically unstable motion. Furthermore, optimization using mocap data in the humanoid full-body joint-space is typically intractable. We propose a new modelfree approach to tractable imitation-based learning in humanoids. We represent kinematic information from human motion capture in a low dimensional subspace and map motor commands in this lowdimensional space to sensory feedback to learn a predictive dynamic model...

• Type: Conference
• Year: 2007
• Where: IJCAI
• Authors: Rawichote Chalodhorn, David B. Grimes, Keith Grochow, Rajesh P. N. Rao
• Keywords: Artificial Intelligence, Humanoid Robot, IJCAI 2007, Mocap Data, Motion Capture