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Cooperative Neighborhood Learning: Application to Robotic Inverse Model

Bruno Dato 1 Marie-Pierre Gleizes 1 Frédéric Migeon 1
1 IRIT-SMAC - Systèmes Multi-Agents Coopératifs
IRIT - Institut de recherche en informatique de Toulouse
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.
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https://hal.archives-ouvertes.fr/hal-03342327
Contributor : Bruno Dato <>
Submitted on : Monday, September 13, 2021 - 11:55:57 AM
Last modification on : Tuesday, September 21, 2021 - 3:38:18 AM

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  • HAL Id : hal-03342327, version 1

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Bruno Dato, Marie-Pierre Gleizes, Frédéric Migeon. Cooperative Neighborhood Learning: Application to Robotic Inverse Model. 13th International Conference on Agents and Artificial Intelligence (ICAART 2021), Feb 2021, Online Streaming, France. pp.368-375. ⟨hal-03342327⟩

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