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Article overview
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BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators | | Fabio Ramos
; Rafael Carvalhaes Possas
; Dieter Fox
; | | Date: |
4 Jun 2019 | | Abstract: | We introduce BayesSim, a framework for robotics simulations allowing a full
Bayesian treatment for the parameters of the simulator. As simulators become
more sophisticated and able to represent the dynamics more accurately,
fundamental problems in robotics such as motion planning and perception can be
solved in simulation and solutions transferred to the physical robot. However,
even the most complex simulator might still not be able to represent reality in
all its details either due to inaccurate parametrization or simplistic
assumptions in the dynamic models. BayesSim provides a principled framework to
reason about the uncertainty of simulation parameters. Given a black box
simulator (or generative model) that outputs trajectories of state and action
pairs from unknown simulation parameters, followed by trajectories obtained
with a physical robot, we develop a likelihood-free inference method that
computes the posterior distribution of simulation parameters. This posterior
can then be used in problems where Sim2Real is critical, for example in policy
search. We compare the performance of BayesSim in obtaining accurate posteriors
in a number of classical control and robotics problems. Results show that the
posterior computed from BayesSim can be used for domain randomization
outperforming alternative methods that randomize based on uniform priors. | | Source: | arXiv, 1906.1728 | | Services: | Forum | Review | PDF | Favorites | |
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