A probabilistic approach to robot trajectory generation

By Alexandros Paraschos, Gerhard Neumann, Jan Peters. 2013

25 citations recorded by the source corpus.

Motor Primitives (MPs) are a promising approach for the data-driven acquisition as well as for the modular and re-usable generation of movements. However, a modular control architecture with MPs is only effective if the MPs support co-activation as well as continuously blending the activation from one MP to the next. In addition, we need efficient mechanisms to adapt a MP to the current situation. Common approaches to movement primitives lack such capabilities or their implementation is based on heuristics. We present a probabilistic movement primitive approach that overcomes the limitations of existing approaches. We encode a primitive as a probability distribution over trajectories. The representation as distribution has several beneficial properties. It allows encoding a time-varying variance profile. Most importantly, it allows performing new operations - a product of distributions for the co-activation of MPs conditioning for generalizing the MP to different desired targets. We derive a feedback controller that reproduces a given trajectory distribution in closed form. We compare our approach to the existing state-of-the art and present real robot results for learning from demonstration.

Subfields: Cognitive Neuroscience, Control and Systems Engineering

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Citations in the corpus

Probabilistic Movement Primitives (2013, background): “While many of these properties are implemented by one or more existing MP architectures [1, 11, 10, 2, 12, 13, 14, 15], no approach exists which exhibits all of these properties in one framework.”