A probabilistic approach to robot trajectory generation

Paraschos, A. and Neumann, G. and Peters, J. (2013) A probabilistic approach to robot trajectory generation. In: 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 15 - 17 October 2013, Atlanta, GA.

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Abstract

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.

Keywords:Robotics
Subjects:H Engineering > H670 Robotics and Cybernetics
G Mathematical and Computer Sciences > G440 Human-computer Interaction
Divisions:College of Science > School of Computer Science
ID Code:25693
Deposited On:19 May 2017 14:47

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