Work

Learning an internal representation of the end-effector configuration space

Year: 2018

Type: preprint

Abstract: Current machine learning techniques proposed to automatically discover a robot kinematics usually rely on a priori information about the robot's structure, sensors properties or end-effector position. This paper proposes a method to estimate a certain aspect of the forward kinematics model with no such information. An internal representation of the end-effector configuration is generated from unstructured proprioceptive and exteroceptive data flow under very limited assumptions. A mapping from the proprioceptive space to this representational space can then be used to control the robot.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1810.01866

Authors: Laflaquière, Alban, Terekhov, Alexander V., Gas, Bruno, O'Regan, J. Kevin

Institutions: Institut Systèmes Intelligents et de Robotique, Université Pierre-et-Marie-Curie, Centre National de la Recherche Scientifique, Laboratoire Psychologie de la Perception, Laboratoire de Physique des Plasmas, Université Paris Descartes

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Audition active binaurale pour des robots humanoïdes

OpenAlex ID: W2950820128

Cites: 7

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Related to: 10