Learning Reusable Manipulation Strategies

• URL: http://arxiv.org/abs/2311.03293v1
• Date: Mon, 6 Nov 2023 17:35:42 GMT
• Title: Learning Reusable Manipulation Strategies
• Authors: Jiayuan Mao, Joshua B. Tenenbaum, Tom\'as Lozano-P\'erez, Leslie Pack Kaelbling
• Abstract summary: Humans demonstrate an impressive ability to acquire and generalize manipulation "tricks" We present a framework that enables machines to acquire such manipulation skills through a single demonstration and self-play. These learned mechanisms and samplers can be seamlessly integrated into standard task and motion planners.
• Score: 86.07442931141634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans demonstrate an impressive ability to acquire and generalize manipulation "tricks." Even from a single demonstration, such as using soup ladles to reach for distant objects, we can apply this skill to new scenarios involving different object positions, sizes, and categories (e.g., forks and hammers). Additionally, we can flexibly combine various skills to devise long-term plans. In this paper, we present a framework that enables machines to acquire such manipulation skills, referred to as "mechanisms," through a single demonstration and self-play. Our key insight lies in interpreting each demonstration as a sequence of changes in robot-object and object-object contact modes, which provides a scaffold for learning detailed samplers for continuous parameters. These learned mechanisms and samplers can be seamlessly integrated into standard task and motion planners, enabling their compositional use.

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