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Authors: Bruno Dato ; Marie-Pierre Gleizes and Frédéric Migeon

Affiliation: Université Toulouse III Paul Sabatier, Toulouse, France

ISBN: 978-989-758-484-8

ISSN: 2184-433X

Keyword(s): Multiagent Learning, Distributed Problem Solving, Learning by Endogenous Feedback, Inverse Kinematics Learning.

Abstract: In this paper we present a generic multiagent learning system based on context learning applied in robotics. By applying learning with multiagent systems in robotics, we propose an endogenous self-learning strategy to improve learning performances. Inspired by constructivism, this learning mechanism encapsulates models in agents. To enhance the learning performance despite the weak amount of data, local and internal negotiation, also called cooperation, is introduced. Agents collaborate by generating artificial learning situations to improve their model. A second contribution is a new exploitation of the learnt models that allows less training. We consider highly redundant robotic arms to learn their Inverse Kinematic Model. A multiagent system learns a collective of models for a robotic arm. The exploitation of the models allows to control the end position of the robotic arm in a 2D/3D space. We show how the addition of artificial learning situations increases the performances of the learnt model and decreases the required labeled learning data. Experimentations are conducted on simulated arms with up to 30 joints in a 2D task space. (More)

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Paper citation in several formats:
Dato, B.; Gleizes, M. and Migeon, F. (2021). Cooperative Neighborhood Learning: Application to Robotic Inverse Model. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-484-8 ISSN 2184-433X, pages 368-375. DOI: 10.5220/0010303203680375

@conference{icaart21,
author={Bruno Dato. and Marie{-}Pierre Gleizes. and Frédéric Migeon.},
title={Cooperative Neighborhood Learning: Application to Robotic Inverse Model},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2021},
pages={368-375},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010303203680375},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Cooperative Neighborhood Learning: Application to Robotic Inverse Model
SN - 978-989-758-484-8
IS - 2184-433X
AU - Dato, B.
AU - Gleizes, M.
AU - Migeon, F.
PY - 2021
SP - 368
EP - 375
DO - 10.5220/0010303203680375

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