OKAMI: Teaching Humanoid Robots Manipulation Skills through Single Video Imitation

• URL: http://arxiv.org/abs/2410.11792v1
• Date: Tue, 15 Oct 2024 17:17:54 GMT
• Title: OKAMI: Teaching Humanoid Robots Manipulation Skills through Single Video Imitation
• Authors: Jinhan Li, Yifeng Zhu, Yuqi Xie, Zhenyu Jiang, Mingyo Seo, Georgios Pavlakos, Yuke Zhu,
• Abstract summary: We introduce OKAMI, a method that generates a manipulation plan from a single RGB-D video. OKAMI uses open-world vision models to identify task-relevant objects and retarget the body motions and hand poses separately.
• Score: 35.97702591413093
• License:
• Abstract: We study the problem of teaching humanoid robots manipulation skills by imitating from single video demonstrations. We introduce OKAMI, a method that generates a manipulation plan from a single RGB-D video and derives a policy for execution. At the heart of our approach is object-aware retargeting, which enables the humanoid robot to mimic the human motions in an RGB-D video while adjusting to different object locations during deployment. OKAMI uses open-world vision models to identify task-relevant objects and retarget the body motions and hand poses separately. Our experiments show that OKAMI achieves strong generalizations across varying visual and spatial conditions, outperforming the state-of-the-art baseline on open-world imitation from observation. Furthermore, OKAMI rollout trajectories are leveraged to train closed-loop visuomotor policies, which achieve an average success rate of 79.2% without the need for labor-intensive teleoperation. More videos can be found on our website https://ut-austin-rpl.github.io/OKAMI/.

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