2026-09-19T12:43:30Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1515422023-08-01T03:45:05Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Shah, Julie A.
Gruenstein, Joshua
Chen, Valerie K.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-07-31T19:47:24Z
2023-07-31T19:47:24Z
2023-06
2023-06-06T16:35:26.788Z
https://hdl.handle.net/1721.1/151542
This thesis proposes advancement of the collaborative and intelligent abilities of Tutor Intelligence robot systems through leveraging the geometry of array structures to perform online inference of object locations and registering partial in-hand scans to automatically orient objects. This research will automate portions of the data annotation process required for the robots’ deep intelligence, enabling the collaborative robot systems to more efficiently and effectively perform pick-and-place tasks. Evaluation is conducted through an exploratory pilot study, and further design recommendations are given.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Grid Inference and Partial Scan Registration for
Intelligent Collaborative Robot Systems
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>This thesis proposes advancement of the collaborative and intelligent abilities of Tutor Intelligence robot systems through leveraging the geometry of array structures to perform online inference of object locations and registering partial in-hand scans to automatically orient objects. This research will automate portions of the data annotation process required for the robots’ deep intelligence, enabling the collaborative robot systems to more efficiently and effectively perform pick-and-place tasks. Evaluation is conducted through an exploratory pilot study, and further design recommendations are given.</Abstract>
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