Test-Time Adaptation of Manipulation Policies Under Actuator Degradation
Overview
TeAR, a policy-agnostic method, adapts manipulation policies in real-time using telemetry data to address actuator degradation, improving success rates across 18 policy-task pairs.
Generated from attributed reports · 4 days agoUpdated
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · success: 31.8 percent · reported trials · Differing source assertions
“TeAR achieves 31.8% success, compared with 25.6% for the base policy and 30.6% for an assumed-model inverse.”
Exact source · revision 1Source owner not reported
Reported quantity · success: 25.6 percent · reported trials · Differing source assertions
“TeAR achieves 31.8% success, compared with 25.6% for the base policy and 30.6% for an assumed-model inverse.”
Exact source · revision 1Source owner not reported
Reported quantity · success: 30.6 percent · reported trials · Differing source assertions
“TeAR achieves 31.8% success, compared with 25.6% for the base policy and 30.6% for an assumed-model inverse.”
Exact source · revision 1Source owner not reported
Reported quantity · improvement: 10 percent · reported trials · Differing source assertions
“TeAR achieves 31.8% success, compared with 25.6% for the base policy and 30.6% for an assumed-model inverse.”
Exact source · revision 1Source owner not reported
Reported quantity · improvement: 15 percent · reported trials · Differing source assertions
“TeAR achieves 31.8% success, compared with 25.6% for the base policy and 30.6% for an assumed-model inverse.”
Exact source · revision 1Source owner not reported
Report timeline
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- arXiv Robotics — research abstractsTest-Time Adaptation of Manipulation Policies Under Actuator Degradation
This research introduces TeAR, a policy-agnostic method that adapts manipulation policies in real-time using telemetry data to account for actuator degradation. Evaluated across 18 policy-task pairs, TeAR improves success rates by 10-15% under heating without requiring on-robot fine-tuning.
Event attention history
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