Imitation Learning and Transferring of Human Movement and Hand Grasping to Adapt to Environment Changes

We propose a model for learning the articulated motion of human arm and hand grasping. The goal is to generate plausible trajectories of joints that mimic the human movement using deformation information. The trajectories are then mapped to a constraint space. These constraints can be the space of start and end configuration of the human body and task-specific constraints such as avoiding an obstacle, picking up and putting down objects. Such a model can be used to develop humanoid robots that move in a human-like way in reaction to diverse changes in their environment and as a priori model for motion tracking. The model proposed to accomplish this uses a combination of principal component analysis (PCA) and a special type of a topological map called the dynamic cell structure (DCS) network. Experiments on arm and hand movements show that this model is able to successfully generalize movement using a few training samples for free movement, obstacle avoidance and grasping objects. We also introduce a method to map the learned human movement to a robot with different geometry using reinforcement learning and show some results.