{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/163571"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/163571","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"DexWrist: A Robotic Wrist for Constrained and Dynamic Manipulation","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_html":"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_has_math":false,"creators":["Ulloa, Gabriella E."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Mechanical Engineering","school":null,"contributors":[],"advisors":["Agrawal, Pulkit"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05","date_published":"2025-05","updated_at":"2026-07-22T22:21:34Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/163571","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Agrawal, Pulkit"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Ulloa, Gabriella E."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-11-05T19:35:22Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-11-05T19:35:22Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Science in Mechanical Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/163571"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["DexWrist: A Robotic Wrist for Constrained and Dynamic Manipulation"]}]}],"canonical_facts":{"dc:contributor.advisor":["Agrawal, Pulkit"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Mechanical Engineering"],"dc:creator":["Ulloa, Gabriella E."],"dc:date.accessioned":["2025-11-05T19:35:22Z"],"dc:date.available":["2025-11-05T19:35:22Z"],"dc:date.issued":["2025-05"],"dc:description.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."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/163571"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["DexWrist: A Robotic Wrist for Constrained and Dynamic Manipulation"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Mechanical Engineering"]},"updated_at":"2026-07-22T22:21:34Z"}