Learning deformations of human arm movement to adapt to environmental constraints

We propose a model for learning the articulated motion of human arm. The goal is to generate plausible trajectories of joints that mimic the human movement using deformation information. The trajectories are then mapped to 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. This movement generalization is a step forward from existing systems that can learn single gestures only. 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. 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 a kinematic chain of 2 joints show that this model is able to successfully generalize movement using a few training samples for both free movement and obstacle avoidance