Co-GAIL: Learning Diverse Strategies for Human-Robot CollaborationDownload PDF

19 Jun 2021, 10:04 (edited 04 Nov 2021)CoRL2021 PosterReaders: Everyone
  • Keywords: Learning for Human-Robot Collaboration, Imitation Learning
  • Abstract: We present a method for learning human-robot collaboration policy from human-human collaboration demonstrations. An effective robot assistant must learn to handle diverse human behaviors shown in the demonstrations and be robust when the humans adjust their strategies during online task execution. Our method co-optimizes a human policy and a robot policy in an interactive learning process: the human policy learns to generate diverse and plausible collaborative behaviors from demonstrations while the robot policy learns to assist by estimating the unobserved latent strategy of its human collaborator. Across a 2D strategy game, a human-robot handover task, and a multi-step collaborative manipulation task, our method outperforms the alternatives in both simulated evaluations and when executing the tasks with a real human operator in-the-loop.
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