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

Abstract

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 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. Supplementary materials and videos at https://sites.google.com/view/co-gail-web/home

arXiv commentCoRL 2021

References in corpus (9)

• Solving Rubik's Cube with a Robot Hand
• Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
• Variational Intrinsic Control
• Multi-Agent Generative Adversarial Imitation Learning
• Variational Option Discovery Algorithms
• Multi-agent Inverse Reinforcement Learning for Two-person Zero-sum Games
• iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes
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Cited by in corpus (3)

• Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
• A Survey of Embodied Learning for Object-Centric Robotic Manipulation
• Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrations