Multiform adaptive robot skill learning from humans

Document Type

Conference Proceeding

Publication Date

1-1-2017

Abstract

Object manipulation is a basic element in everyday human lives. Robotic manipulation has progressed from maneuvering single-rigid-body objects with firm grasping to maneuvering soft objects and handling contact-rich actions. Meanwhile, technologies such as robot learning from demonstration have enabled humans to intuitively train robots. This paper discusses a new level of robotic learning-based manipulation. In contrast to the single form of learning from demonstration, we propose a multiform learning approach that integrates additional forms of skill acquisition, including adaptive learning from definition and evaluation. Moreover, going beyond state-of-the-art technologies of handling purely rigid or soft objects in a pseudo-static manner, our work allows robots to learn to handle partly rigid partly soft objects with time-critical skills and sophisticated contact control. Such capability of robotic manipulation offers a variety of new possibilities in human-robot interaction.

Identifier

85036614291 (Scopus)

ISBN

[9780791858271]

Publication Title

ASME 2017 Dynamic Systems and Control Conference Dscc 2017

External Full Text Location

https://doi.org/10.1115/DSCC2017-5114

Volume

1

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