Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks

• URL: http://arxiv.org/abs/2202.10765v1
• Date: Tue, 22 Feb 2022 09:35:09 GMT
• Title: Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks
• Authors: Hongtao Wu, Jikai Ye, Xin Meng, Chris Paxton, Gregory Chirikjian
• Abstract summary: Transporters with Visual Foresight (TVF) is able to achieve multi-task learning and zero-shot generalization to unseen tasks. TVF is able to improve the performance of a state-of-the-art imitation learning method on both training and unseen tasks in simulation and real robot experiments.
• Score: 12.604533231243543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rearrangement tasks have been identified as a crucial challenge for intelligent robotic manipulation, but few methods allow for precise construction of unseen structures. We propose a visual foresight model for pick-and-place manipulation which is able to learn efficiently. In addition, we develop a multi-modal action proposal module which builds on Goal-Conditioned Transporter Networks, a state-of-the-art imitation learning method. Our method, Transporters with Visual Foresight (TVF), enables task planning from image data and is able to achieve multi-task learning and zero-shot generalization to unseen tasks with only a handful of expert demonstrations. TVF is able to improve the performance of a state-of-the-art imitation learning method on both training and unseen tasks in simulation and real robot experiments. In particular, the average success rate on unseen tasks improves from 55.0% to 77.9% in simulation experiments and from 30% to 63.3% in real robot experiments when given only tens of expert demonstrations. More details can be found on our project website: https://chirikjianlab.github.io/tvf/

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