Robust Policy Search for Robot Navigation with Stochastic Meta-Policies

• URL: http://arxiv.org/abs/2003.01000v1
• Date: Mon, 2 Mar 2020 16:30:59 GMT
• Title: Robust Policy Search for Robot Navigation with Stochastic Meta-Policies
• Authors: Javier Garcia-Barcos, Ruben Martinez-Cantin
• Abstract summary: In this work, we exploit the main ingredients of Bayesian optimization to provide robustness to different issues for policy search algorithms. We combine several methods and show how their interaction works better than the sum of the parts. We compare the proposed algorithm with previous results in several optimization benchmarks and robot tasks, such as pushing objects with a robot arm, or path finding with a rover.
• Score: 5.7871177330714145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian optimization is an efficient nonlinear optimization method where the queries are carefully selected to gather information about the optimum location. Thus, in the context of policy search, it has been called active policy search. The main ingredients of Bayesian optimization for sample efficiency are the probabilistic surrogate model and the optimal decision heuristics. In this work, we exploit those to provide robustness to different issues for policy search algorithms. We combine several methods and show how their interaction works better than the sum of the parts. First, to deal with input noise and provide a safe and repeatable policy we use an improved version of unscented Bayesian optimization. Then, to deal with mismodeling errors and improve exploration we use stochastic meta-policies for query selection and an adaptive kernel. We compare the proposed algorithm with previous results in several optimization benchmarks and robot tasks, such as pushing objects with a robot arm, or path finding with a rover.

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