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arXiv · 1708.05192

Multiform Adaptive Robot Skill Learning from Humans

Leidi Zhao·Raheem Lawhorn·Siddharth Patil·Steve Susanibar·Lu Lu·Cong Wang·Bo Ouyang
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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.

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Leidi Zhao, Raheem Lawhorn, Siddharth Patil, Steve Susanibar, Lu Lu, Cong Wang, Bo Ouyang. 2017-08-17. Multiform Adaptive Robot Skill Learning from Humans. https://arxiv.org/abs/1708.05192

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