Using eigenposes for lossless periodic human motion imitation

— Programming a humanoid robot to perform an action that takes the robot’s complex dynamics into account is a challenging problem. Traditional approaches typically require highly accurate prior knowledge of the robot’s dynamics and environment in order to devise complex control algorithms for generating a stable dynamic motion. Training using human motion capture is an intuitive and flexible approach to programming a robot but directly applying motion capture data to a robot usually results in dynamically unstable motion. Optimization using high-dimensional motion capture data in the humanoid full-body joint-space is also typically intractable. In previous work, we proposed an approach that uses dimensionality reduction to achieve tractable imitation-based learning in humanoids without the need for a physics-based dynamics model. This work was based on a 3-D “eigenpose” representation. However, for some motion patterns, using only three dimensions for eigenposes is insuffic...

• Type: Conference
• Year: 2009
• Where: IROS
• Authors: Rawichote Chalodhorn, Rajesh P. N. Rao
• Keywords: Humanoid Robot, IROS 2009, Motion Capture, Motion Capture Data, Robotics