Motion Generation with Geodesic Paths on Learnt Skill Manifolds

Abstract

We present a framework for generating motions drawn from parametrized classes of motions and in response to goals chosen arbitrarily from a set. Our framework is based on learning a manifold representation of possible trajectories, from a set of example trajectories that are generated by a (computationally expensive) process of optimization. We show that these examples can be utilized to learn a manifold on which all feasible trajectories corresponding to a skill are the geodesics. This manifold is learned by inferring the local tangent spaces from data. Our main result is that this process allows us to define a flexible and computationally efficient motion generation procedure that comes close to the much more expensive computational optimization procedure in terms of accuracy while taking a small fraction of the time to perform a similar computation.

An extended version of this paper appears in [4].