Paraschos, A., 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.
Documents |
|
![]() |
PDF
25693 07030017.pdf - Whole Document Restricted to Repository staff only 1MB |
Item Type: | Conference or Workshop contribution (Paper) |
---|---|
Item Status: | Live Archive |
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 |
Related URLs: | |
ID Code: | 25693 |
Deposited On: | 19 May 2017 14:47 |
Repository Staff Only: item control page