One-Shot Imitation under Mismatched Execution

• URL: http://arxiv.org/abs/2409.06615v4
• Date: Wed, 16 Oct 2024 02:19:05 GMT
• Title: One-Shot Imitation under Mismatched Execution
• Authors: Kushal Kedia, Prithwish Dan, Angela Chao, Maximus Adrian Pace, Sanjiban Choudhury,
• Abstract summary: Human demonstrations as prompts are a powerful way to program robots to do long-horizon manipulation tasks. translating these demonstrations into robot-executable actions presents significant challenges due to execution mismatches in movement styles and physical capabilities. We propose RHyME, a novel framework that automatically aligns human and robot task executions using optimal transport costs.
• Score: 7.060120660671016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human demonstrations as prompts are a powerful way to program robots to do long-horizon manipulation tasks. However, translating these demonstrations into robot-executable actions presents significant challenges due to execution mismatches in movement styles and physical capabilities. Existing methods either depend on human-robot paired data, which is infeasible to scale, or rely heavily on frame-level visual similarities that often break down in practice. To address these challenges, we propose RHyME, a novel framework that automatically aligns human and robot task executions using optimal transport costs. Given long-horizon robot demonstrations, RHyME synthesizes semantically equivalent human videos by retrieving and composing short-horizon human clips. This approach facilitates effective policy training without the need for paired data. RHyME successfully imitates a range of cross-embodiment demonstrators, both in simulation and with a real human hand, achieving over 50\% increase in task success compared to previous methods. We release our datasets and graphics at this https://portal.cs.cornell.edu/rhyme/.

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