2026-09-18T21:19:23Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1565612024-09-04T04:05:27Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Agrawal, Pulkit
Karpoor, Shreya S.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2024-09-03T21:07:36Z
2024-09-03T21:07:36Z
2024-05
2024-07-11T14:36:19.250Z
https://hdl.handle.net/1721.1/156561
Imitation learning has shown promising results in teaching robots new skills. We propose augmenting the ALOHA bimanual teleoperation system with haptic feedback to obtain higher quality expert demonstrations. We add two types of haptic feedback: force feedback and cutaneous feedback in both a real and simulation teleoperation system. Additionally, we propose to add tactile sensors to observe the impact of tactile data to imitation learning models in solving fine manipulation tasks.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Force Feedback and Tactile Sensing for Robotic Teleoperation of Contact Rich Manipulation Tasks
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>Imitation learning has shown promising results in teaching robots new skills. We propose augmenting the ALOHA bimanual teleoperation system with haptic feedback to obtain higher quality expert demonstrations. We add two types of haptic feedback: force feedback and cutaneous feedback in both a real and simulation teleoperation system. Additionally, we propose to add tactile sensors to observe the impact of tactile data to imitation learning models in solving fine manipulation tasks.</Abstract>
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