Human-in-the-Loop Task and Motion Planning for Imitation Learning

• URL: http://arxiv.org/abs/2310.16014v1
• Date: Tue, 24 Oct 2023 17:15:16 GMT
• Title: Human-in-the-Loop Task and Motion Planning for Imitation Learning
• Authors: Ajay Mandlekar, Caelan Garrett, Danfei Xu, Dieter Fox
• Abstract summary: Imitation learning from human demonstrations can teach robots complex manipulation skills, but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) systems are automated and excel at solving long-horizon tasks. We present Human-in-the-Loop Task and Motion Planning (HITL-TAMP), a novel system that leverages the benefits of both approaches.
• Score: 37.75197145733193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Imitation learning from human demonstrations can teach robots complex manipulation skills, but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) systems are automated and excel at solving long-horizon tasks, but they are difficult to apply to contact-rich tasks. In this paper, we present Human-in-the-Loop Task and Motion Planning (HITL-TAMP), a novel system that leverages the benefits of both approaches. The system employs a TAMP-gated control mechanism, which selectively gives and takes control to and from a human teleoperator. This enables the human teleoperator to manage a fleet of robots, maximizing data collection efficiency. The collected human data is then combined with an imitation learning framework to train a TAMP-gated policy, leading to superior performance compared to training on full task demonstrations. We compared HITL-TAMP to a conventional teleoperation system -- users gathered more than 3x the number of demos given the same time budget. Furthermore, proficient agents (75\%+ success) could be trained from just 10 minutes of non-expert teleoperation data. Finally, we collected 2.1K demos with HITL-TAMP across 12 contact-rich, long-horizon tasks and show that the system often produces near-perfect agents. Videos and additional results at https://hitltamp.github.io .

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