Dynamic Primitives Facilitate Manipulating a Whip
© 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 prominen...
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022
|
| Online Access: | https://hdl.handle.net/1721.1/141415 |
| _version_ | 1826207707832516608 |
|---|---|
| author | Nah, Moses C Krotov, Aleksei Russo, Marta Sternad, Dagmar Hogan, Neville |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Nah, Moses C Krotov, Aleksei Russo, Marta Sternad, Dagmar Hogan, Neville |
| author_sort | Nah, Moses C |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T13:53:43Z |
| format | Article |
| id | mit-1721.1/141415 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:53:43Z |
| publishDate | 2022 |
| publisher | IEEE |
| record_format | dspace |
| spelling | mit-1721.1/1414152023-02-10T21:30:46Z Dynamic Primitives Facilitate Manipulating a Whip Nah, Moses C Krotov, Aleksei Russo, Marta Sternad, Dagmar Hogan, Neville Massachusetts Institute of Technology. Department of Mechanical Engineering Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences © 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. 2022-03-30T17:18:45Z 2022-03-30T17:18:45Z 2020 2022-03-30T17:12:38Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/141415 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. en 10.1109/BIOROB49111.2020.9224399 Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IEEE Prof. Hogan via Elizabeth Kuhlman |
| spellingShingle | Nah, Moses C Krotov, Aleksei Russo, Marta Sternad, Dagmar Hogan, Neville Dynamic Primitives Facilitate Manipulating a Whip |
| title | Dynamic Primitives Facilitate Manipulating a Whip |
| title_full | Dynamic Primitives Facilitate Manipulating a Whip |
| title_fullStr | Dynamic Primitives Facilitate Manipulating a Whip |
| title_full_unstemmed | Dynamic Primitives Facilitate Manipulating a Whip |
| title_short | Dynamic Primitives Facilitate Manipulating a Whip |
| title_sort | dynamic primitives facilitate manipulating a whip |
| url | https://hdl.handle.net/1721.1/141415 |
| work_keys_str_mv | AT nahmosesc dynamicprimitivesfacilitatemanipulatingawhip AT krotovaleksei dynamicprimitivesfacilitatemanipulatingawhip AT russomarta dynamicprimitivesfacilitatemanipulatingawhip AT sternaddagmar dynamicprimitivesfacilitatemanipulatingawhip AT hoganneville dynamicprimitivesfacilitatemanipulatingawhip |