Planning-Space Shift Learning: Variable-space Motion Planning toward Flexible Extension of Body Schema

To improve the flexibility of robotic learning, it is important to realize an ability to generate a hierarchical structure. This paper proposes a learning framework which can dynamically change the planning space depending on the structure of tasks. Synchronous motion information is utilized to generate modes and different modes correspond to different hierarchical structure of the controller. This enables efficient task planning and control using low-dimensional space. An object manipulation task is tested as an application, where an object is found and used as a tool (or as a part of the body) to extend the ability of the robot. The proposed framework is expected to be a basic learning model to account for body image acquisition including tool affordances.