Extracting low-dimensional control variables for movement primitives

Rueckert, E., Mundo, J., Paraschos, A. , Peters, J. and Neumann, Gerhard (2015) Extracting low-dimensional control variables for movement primitives. In: IEEE International Conference on Robotics and Automation 2015, 26-30 May 2015, Washington State Convention Center, Seattle.

Rueckert_ICRA14LMProMPsFinal.pdf - Whole Document

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


Movement primitives (MPs) provide a powerful framework for data driven movement generation that has been successfully applied for learning from demonstrations and robot reinforcement learning. In robotics we often want to solve a multitude of different, but related tasks. As the parameters of the primitives are typically high dimensional, a common practice for the generalization of movement primitives to new tasks is to adapt only a small set of control variables, also called meta parameters, of the primitive. Yet, for most MP representations, the encoding of these control variables is pre-coded in the representation and can not be adapted to the considered tasks. In this paper, we want to learn the encoding of task-specific control variables also from data instead of relying on fixed meta-parameter representations. We use hierarchical Bayesian models (HBMs) to estimate a low dimensional latent variable model for probabilistic movement primitives (ProMPs), which is a recent movement primitive representation. We show on two real robot datasets that ProMPs based on HBMs outperform standard ProMPs in terms of generalization and learning from a small amount of data and also allows for an intuitive analysis of the movement. We also extend our HBM by a mixture model, such that we can model different movement types in the same dataset.

Keywords:Dimensionality Reduction, Movement Primitives, Robotics, Structure Learning
Subjects:H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
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ID Code:25760
Deposited On:03 Feb 2017 11:05

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