Multiform adaptive robot skill learning from humans

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationAerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systems
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791858271
StatePublished - 2017
EventASME 2017 Dynamic Systems and Control Conference, DSCC 2017 - Tysons, United States
Duration: Oct 11 2017Oct 13 2017

Publication series

NameASME 2017 Dynamic Systems and Control Conference, DSCC 2017


OtherASME 2017 Dynamic Systems and Control Conference, DSCC 2017
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering


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