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Experience-based Learning Mechanism for Neural Controller Adaptation: Application to Walking Biped Robots

Abstract : Neurobiology studies showed that the role of the Anterior Cingulate Cortex of the brain is primarily responsible for avoiding repeated mistakes. According to vigilance threshold, which denotes the tolerance to risks, we can differentiate between a learning mechanism that takes risks, and one that averts risks. The tolerance to risk plays an important role in such learning mechanism. Results have shown the differences in learning capacity between risk-taking and risk avert behaviors. In this paper we propose a learning mechanism that is able to learn from negative and positive feedback. It is composed of two phases, evaluation and decision-making phase. In the evaluation phase, we use a Kohonen Self Organizing Map technique to represent success and failure. Decision-making is based on an early warning mechanism that enables to avoid repeating past mistakes. Our approach is presented with an implementation on a simulated planar biped robot, controlled by a reflexive lowlevel neural controller. The learning system adapts the dynamics and range of a hip sensor neuron of the controller in order for the robot to walk on flat and slope terrain. Results show that success and failure maps can learn better with a threshold that is more tolerant to risk. This gives rise to robustness to the controller even in the presence of slope variations.
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https://hal.archives-ouvertes.fr/hal-00519941
Contributor : Patrick Henaff <>
Submitted on : Tuesday, September 21, 2010 - 5:30:58 PM
Last modification on : Monday, January 25, 2021 - 3:16:03 PM

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  • HAL Id : hal-00519941, version 1

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John Nassour, Patrick Henaff, Fethi Ben Ouezdou, Gordon Cheng. Experience-based Learning Mechanism for Neural Controller Adaptation: Application to Walking Biped Robots. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009 ), Sep 2009, France. pp.4955-4961. ⟨hal-00519941⟩

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