Model-free Probabilistic Movement Primitives for physical interaction

Paraschos, A., Rueckert, E., Peters, J. and Neumann, G. (2015) Model-free Probabilistic Movement Primitives for physical interaction. In: IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 28 September - 2 October 2015, Hamburg, Germany.

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Item Type:Conference or Workshop contribution (Paper)
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Abstract

Physical interaction in robotics is a complex problem
that requires not only accurate reproduction of the kinematic
trajectories but also of the forces and torques exhibited
during the movement. We base our approach on Movement
Primitives (MP), as MPs provide a framework for modelling
complex movements and introduce useful operations on the
movements, such as generalization to novel situations, time
scaling, and others. Usually, MPs are trained with imitation
learning, where an expert demonstrates the trajectories. However,
MPs used in physical interaction either require additional
learning approaches, e.g., reinforcement learning, or are based
on handcrafted solutions. Our goal is to learn and generate
movements for physical interaction that are learned with imitation
learning, from a small set of demonstrated trajectories.
The Probabilistic Movement Primitives (ProMPs) framework
is a recent MP approach that introduces beneficial properties,
such as combination and blending of MPs, and represents the
correlations present in the movement. The ProMPs provides
a variable stiffness controller that reproduces the movement
but it requires a dynamics model of the system. Learning such
a model is not a trivial task, and, therefore, we introduce the
model-free ProMPs, that are learning jointly the movement and
the necessary actions from a few demonstrations. We derive
a variable stiffness controller analytically. We further extent
the ProMPs to include force and torque signals, necessary for
physical interaction. We evaluate our approach in simulated
and real robot tasks.

Keywords:Movement Primitives, Physical Interaction
Subjects:H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:25752
Deposited On:02 Feb 2017 16:14

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