Corpus ID: 237048121

Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

@article{Wang2021CoGAILLD,
  title={Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration},
  author={Chen Wang and Claudia P'erez-D'Arpino and Danfei Xu and Li Fei-Fei and C. Karen Liu and Silvio Savarese},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.06038}
}
We present a method for learning a 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… Expand

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