Universal Visual Decomposer: Long-Horizon Manipulation Made Easy

• URL: http://arxiv.org/abs/2310.08581v1
• Date: Thu, 12 Oct 2023 17:59:41 GMT
• Title: Universal Visual Decomposer: Long-Horizon Manipulation Made Easy
• Authors: Zichen Zhang, Yunshuang Li, Osbert Bastani, Abhishek Gupta, Dinesh Jayaraman, Yecheng Jason Ma, Luca Weihs
• Abstract summary: Real-world robotic tasks stretch over extended horizons and encompass multiple stages. Prior task decomposition methods require task-specific knowledge, are computationally intensive, and cannot readily be applied to new tasks. We propose Universal Visual Decomposer (UVD), an off-the-shelf task decomposition method for visual long horizon manipulation. We extensively evaluate UVD on both simulation and real-world tasks, and in all cases, UVD substantially outperforms baselines across imitation and reinforcement learning settings.
• Score: 54.93745986073738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-world robotic tasks stretch over extended horizons and encompass multiple stages. Learning long-horizon manipulation tasks, however, is a long-standing challenge, and demands decomposing the overarching task into several manageable subtasks to facilitate policy learning and generalization to unseen tasks. Prior task decomposition methods require task-specific knowledge, are computationally intensive, and cannot readily be applied to new tasks. To address these shortcomings, we propose Universal Visual Decomposer (UVD), an off-the-shelf task decomposition method for visual long horizon manipulation using pre-trained visual representations designed for robotic control. At a high level, UVD discovers subgoals by detecting phase shifts in the embedding space of the pre-trained representation. Operating purely on visual demonstrations without auxiliary information, UVD can effectively extract visual subgoals embedded in the videos, while incurring zero additional training cost on top of standard visuomotor policy training. Goal-conditioned policies learned with UVD-discovered subgoals exhibit significantly improved compositional generalization at test time to unseen tasks. Furthermore, UVD-discovered subgoals can be used to construct goal-based reward shaping that jump-starts temporally extended exploration for reinforcement learning. We extensively evaluate UVD on both simulation and real-world tasks, and in all cases, UVD substantially outperforms baselines across imitation and reinforcement learning settings on in-domain and out-of-domain task sequences alike, validating the clear advantage of automated visual task decomposition within the simple, compact UVD framework.

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