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Leveraging Multiple Environments for Learning and Decision Making: a Dismantling Use Case

Abstract : Learning is usually performed by observing real robot executions. Physics-based simulators are a good alternative for providing highly valuable information while avoiding costly and potentially destructive robot executions. We present a novel approach for learning the probabilities of symbolic robot action outcomes. This is done leveraging different environments, such as physics-based simulators, in execution time. To this end, we propose MENID (Multiple Environment Noise Indeterministic Deictic) rules, a novel representation able to cope with the inherent uncertainties present in robotic tasks. MENID rules explicitly represent each possible outcomes of an action, keep memory of the source of the experience, and maintain the probability of success of each outcome. We also introduce an algorithm to distribute actions among environments, based on previous experiences and expected gain. Before using physicsbased simulations, we propose a methodology for evaluating different simulation settings and determining the least timeconsuming model that could be used while still producing coherent results. We demonstrate the validity of the approach in a dismantling use case, using a simulation with reduced quality as simulated system, and a simulation with full resolution where we add noise to the trajectories and some physical parameters as a representation of the real system.
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Conference papers
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https://hal.inria.fr/hal-03132986
Contributor : Eric Marchand Connect in order to contact the contributor
Submitted on : Friday, February 5, 2021 - 3:28:03 PM
Last modification on : Wednesday, November 3, 2021 - 8:17:03 AM
Long-term archiving on: : Friday, May 7, 2021 - 8:27:48 AM

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Suarez_IROS2020.pdf
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  • HAL Id : hal-03132986, version 1

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Alejandro Suárez-Hernández, Thierry Gaugry, Javier Segovia-Aguas, Antonin Bernardin, Carme Torras, et al.. Leveraging Multiple Environments for Learning and Decision Making: a Dismantling Use Case. IROS 2020 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2020, Las Vegas / Virtual, United States. pp.6902-6908. ⟨hal-03132986⟩

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