Pignat, E., Lembono, T.S. and Calinon, S. (2020)
Variational Inference with Mixture Model Approximation for Applications in Robotics
In Proc. IEEE Intl Conf. on Robotics and Automation (ICRA).

Abstract

We propose to formulate the problem of representing a distribution of robot configurations (e.g. joint angles) as that of approximating a product of experts. Our approach uses variational inference, a popular method in Bayesian computation, which has several practical advantages over sampling-based techniques. To be able to represent complex and multimodal distributions of configurations, mixture models are used as approximate distribution. We show that the problem of approximating a distribution of robot configurations while satisfying multiple objectives arises in a wide range of problems in robotics, for which the properties of the proposed approach have relevant consequences. Several applications are discussed, including learning objectives from demonstration, planning, and warm-starting inverse kinematics problems. Simulated experiments are presented with a 7-DoF Panda arm and a 28-DoF Talos humanoid.

Bibtex reference

@inproceedings{Pignat20ICRA,
	author="Pignat, E. and Lembono, T. S. and Calinon, S.",
	title="Variational Inference with Mixture Model Approximation for Applications in Robotics",
	booktitle="Proc. IEEE Intl Conf. on Robotics and Automation (ICRA)",
	year="2020",
	pages=""
}
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