This course will present various ways of representing movement data and gestures in a mathematical manner, with the goal of analyzing, compressing or generating movements. Several examples of applications will be covered, from generation of manipulation skills in robotics to the analysis of motion capture data. The principle of movement primitives will be presented, which allows reorganization in parallel and in series of ‘motion bricks’, in order to create new gestures or to adapt a gesture to a new situation or to a new kinematic chain. Several movement representations will be covered in the course, arising from different research domains, including statistical modeling (hidden Markov models), differential geometry (Riemannian manifolds) and dynamic systems (dynamic movement primitives).
Sylvain Calinon |AIMove’s Academic Director for IDIAP