Publication

Leveraging multiple environments for learning and decision making: a dismantling use case

Conference Article

Conference

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Edition

2020

Pages

6902-6908

Doc link

https://dx.doi.org/10.1109/IROS45743.2020.9341182

File

Download the digital copy of the doc pdf document

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 physics-based simulations, we propose a methodology for evaluating different simulation settings and determining the least time-consuming 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.

Categories

learning (artificial intelligence), planning (artificial intelligence).

Author keywords

Uncertainty, Decision making, Simulation

Scientific reference

A. Suárez, T. Gaugry, J. Segovia, A. Bernardin, C. Torras, M. Marchal and G. Alenyà. Leveraging multiple environments for learning and decision making: a dismantling use case, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020, Las Vegas, NV, USA, pp. 6902-6908.