Dynamic primitives facilitate manipulating a whip
Author(s)
Nah, Moses C.![Thumbnail](/bitstream/handle/1721.1/127121/1191836862-MIT.pdf.jpg?sequence=4&isAllowed=y)
Download1191836862-MIT.pdf (9.446Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Advisor
Neville Hogan.
Terms of use
Metadata
Show full item recordAbstract
Human dexterity far exceeds that of modern robots, despite a much slower neuromuscular system. Understanding how this is accomplished may lead to improved robot control. The slow neuromuscular system of humans implies that prediction based on some form of internal model plays a prominent role. However, the nature of the model itself remains unclear. To address this problem, we focused on one of the most complex and exotic tools humans can manipulate -- a whip. We tested (in simulation) whether a distant target could be reached with a whip using a (small) number of dynamic primitives, whose parameters could be learned through optimization. This approach was able to manage the complexity of an (extremely) high degree-of-freedom system and discovered the optimal parameters of the upper-limb movement that achieved the task. A detailed model of the whip dynamics was not needed for this approach, which thereby significantly relieved the computational burden of task representation and performance optimization. These results support our hypothesis that composing control using dynamic motor primitives may be a strategy which humans use to enable their remarkable dexterity. A similar approach may contribute to improved robot control..
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020 Cataloged from the official PDF of thesis. Includes bibliographical references (pages 127-133).
Date issued
2020Department
Massachusetts Institute of Technology. Department of Mechanical EngineeringPublisher
Massachusetts Institute of Technology
Keywords
Mechanical Engineering.