Probabilistic Movement PrimitivesAlexandros Paraschos; Christian Daniel; Jan Peters; Gerhard Neumann
In: Christopher J. C. Burges; Léon Bottou; Zoubin Ghahramani; Kilian Q. Weinberger (Hrsg.). Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Neural Information Processing Systems (NeurIPS-2013), December 5-8, Lake Tahoe, Nevada, USA, Pages 2616-2624, Curran Associates, Inc. 2013.
Movement Primitives (MP) are a well-established approach for representing modular and re-usable robot movement generators. Many state-of-the-art robot learning successes are based MPs, due to their compact representation of the inherently continuous and high dimensional robot movements. A major goal in robot learning is to combine multiple MPs as building blocks in a modular control architecture to solve complex tasks. To this effect, a MP representation has to allow for blending between motions, adapting to altered task variables, and co-activating multiple MPs in parallel. We present a probabilistic formulation of the MP concept that maintains a distribution over trajectories. Our probabilistic approach allows for the derivation of new operations which are essential for implementing all aforementioned properties in one framework. In order to use such a trajectory distribution for robot movement control, we analytically derive a stochastic feedback controller which reproduces the given trajectory distribution. We evaluate and compare our approach to existing methods on several simulated as well as real robot scenarios.