Efficiently Learning Recoveries from Failures Under Partial Observability. (arXiv:2209.13605v1 [cs.RO])

Operating under real world conditions is challenging due to the possibility
of a wide range of failures induced by partial observability. In relatively
benign settings, such failures can be overcome by retrying or executing one of
a small number of hand-engineered recovery strategies. By contrast,
contact-rich sequential manipulation tasks, like opening doors and assembling
furniture, are not amenable to exhaustive hand-engineering. To address this
issue, we present a general approach for robustifying manipulation strategies
in a sample-efficient manner. Our approach incrementally improves robustness by
first discovering the failure modes of the current strategy via exploration in
simulation and then learning additional recovery skills to handle these
failures. To ensure efficient learning, we propose an online algorithm Value
Upper Confidence Limit (Value-UCL) that selects what failure modes to
prioritize and which state to recover to such that the expected performance
improves maximally in every training episode. We use our approach to learn
recovery skills for door-opening and evaluate them both in simulation and on a
real robot with little fine-tuning. Compared to open-loop execution, our
experiments show that even a limited amount of recovery learning improves task
success substantially from 71% to 92.4% in simulation and from 75% to 90%
on a real robot.

Source: https://arxiv.org/abs/2209.13605

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