A probabilistic approach to robot trajectory generation

Paraschos, A., Neumann, Gerhard and Peters, J. (2015) A probabilistic approach to robot trajectory generation. In: International Conference on Humanoid Robots (HUMANOIDS), 15-17 October 2013, Atlanta, GA.

Paraschos_Humanoids_2013.pdf - Whole Document

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


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:Movement Primitives
Subjects:H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
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ID Code:25755
Deposited On:28 Jul 2017 07:50

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