Dynamic Primitives Facilitate Manipulating a Whip
Name
Moses Biorob 2020.pdf
Description
Accepted version
Size
1.79 MB
Format
Adobe PDF
Checksum (MD5)
7baeb680194b510cb36ee242a1984e5b
Author(s)
Nah, Moses C
Krotov, Aleksei
Russo, Marta
Sternad, Dagmar
Hogan, Neville
Date Issued
2020
Journal
Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
Publisher
IEEE
Citation
Nah, Moses C, Krotov, Aleksei, Russo, Marta, Sternad, Dagmar and Hogan, Neville. 2020. "Dynamic Primitives Facilitate Manipulating a Whip." Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, 2020-November.
Version
Author's final manuscript
Abstract
© 2020 IEEE. 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 five optimal parameters of a single movement that achieved the task. An internal model of the whip dynamics was not needed for this approach, thereby significantly relieving 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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/BIOROB49111.2020.9224399
