Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations. (arXiv:2206.11693v1 [cs.RO])

Web: http://arxiv.org/abs/2206.11693

Learning agile skills is one of the main challenges in robotics. To this end,
reinforcement learning approaches have achieved impressive results. These
methods require explicit task information in terms of a reward function or an
expert that can be queried in simulation to provide a target control output,
which limits their applicability. In this work, we propose a generative
adversarial method for inferring reward functions from partial and potentially
physically incompatible demonstrations for successful skill acquirement where
reference or expert …

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