2026-09-19T12:15:51Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1635712025-11-06T03:06:17Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Agrawal, Pulkit
Ulloa, Gabriella E.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2025-11-05T19:35:22Z
2025-11-05T19:35:22Z
2025-05
2025-06-26T14:15:37.693Z
https://hdl.handle.net/1721.1/163571
0009-0004-2966-9039
DexWrist is a compliant robotic wrist designed to advance robotic manipulation in highly-constrained environments, enable dynamic tasks, and speed up data collection. DexWrist is designed to be close to the functional capabilities of the human wrist and achieves mechanical compliance and a greater workspace as compared to existing robotic wrist designs. The DexWrist can supercharge policy learning by (i) enabling faster teleoperation and therefore making data collection more scalable; (ii) completing tasks in fewer steps which reduces trajectory lengths and therefore can ease policy learning; (iii) DexWrist is designed to be torque transparent with easily simulateable kinematics for simulated data collection; and most importantly (iv) expands the workspace of manipulation for approaching highly cluttered scenes and tasks. More details about the wrist can be found at: https://sites.google.com/view/dexwrist/home.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
DexWrist: A Robotic Wrist for Constrained and Dynamic Manipulation
Thesis
application/pdf
Master
Master of Science in Mechanical Engineering
Publication
unknown
unknown
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<Abstract>DexWrist is a compliant robotic wrist designed to advance robotic manipulation in highly-constrained environments, enable dynamic tasks, and speed up data collection. DexWrist is designed to be close to the functional capabilities of the human wrist and achieves mechanical compliance and a greater workspace as compared to existing robotic wrist designs. The DexWrist can supercharge policy learning by (i) enabling faster teleoperation and therefore making data collection more scalable; (ii) completing tasks in fewer steps which reduces trajectory lengths and therefore can ease policy learning; (iii) DexWrist is designed to be torque transparent with easily simulateable kinematics for simulated data collection; and most importantly (iv) expands the workspace of manipulation for approaching highly cluttered scenes and tasks. More details about the wrist can be found at: https://sites.google.com/view/dexwrist/home.</Abstract>
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