Integrated Object, Skill, and Motion Models for Nonprehensile Manipulation

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

Advanced hand skills for object manipulation can greatly enhance the physical capability of robots in a variety of applications. Models that can comprehensively and ubiquitously capture semantic information from the demonstration data are essential for robots to learn skills and act autonomously. Compared to object manipulation with firm grasping, nonprehensile manipulation skills can significantly extend the manipulation ability of robots but are also challenging to model. This paper introduces several new modeling techniques for nonprehensile object manipulation and their integration for robot learning and control. Other than a basic map of the object's state transitions, the proposed modeling framework includes a generic object model that can help a learning agent infer manipulations that have not been demonstrated, a contact-based skill model that can semantically describe nonprehensile manipulation skills, and a motion model that can incrementally identify patterns from crowdsourced and constantly collected data. Examples and experiment results are given to explain and validate the proposed methods.

Identifier

85203287564 (Scopus)

ISBN

[9798350355369]

Publication Title

IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM

External Full Text Location

https://doi.org/10.1109/AIM55361.2024.10637220

e-ISSN

21596255

ISSN

21596247

First Page

184

Last Page

191

Grant

1944069

Fund Ref

National Science Foundation

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