Learning Reusable Manipulation StrategiesDownload PDF

Published: 30 Aug 2023, Last Modified: 29 Oct 2023CoRL 2023 PosterReaders: Everyone
Keywords: Contact Modeling and Manipulation, Task and Motion Planning
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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Website: https://concepts-ai.com/p/mechanisms
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