  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.
