Polymorphic Robot Learning for Dynamic and Contact-Rich Handling of Soft-Rigid Objects

Raheem Lawhorn, Steve Susanibar, Lu Lu, Cong Wang

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

6 Scopus citations

Abstract

In the operation of robots in regular human lives, the capability of object handling is of fundamental importance. Robotic manipulation has gone from handling single rigid body objects with firm grasping to handling soft objects and dealing with slip and contact. Meanwhile, technologies such as robot learning from demonstration has enabled intuitive human-to-robot teaching. This paper discusses a new level of robotic learning-based manipulation. Instead of the single form of learning from demonstration, we propose a polymorphic learning scheme that integrates additional types of robot skill acquiring, including adaptive definition and evaluation. In addition, compared to the current studies of handling pure rigid or soft objects in a pseudo-static manner, our work aims to allow robots to learn to manipulate objects that are partly soft partly rigid, require time-critical dynamic skills and subtle contact control, such as handling tethered tools and even using martial arts instruments. This type of tasks, once successfully robotized, open a variety of new possibilities in robot-human coexistence.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages596-601
Number of pages6
ISBN (Electronic)9781509059980
DOIs
StatePublished - Aug 21 2017
Event2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017 - Munich, Germany
Duration: Jul 3 2017Jul 7 2017

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM

Other

Other2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017
Country/TerritoryGermany
CityMunich
Period7/3/177/7/17

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software

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