Retrieve in Time, Correct in Frequency (RTCF)
Overview
The paper presents RTCF, a training-free test-time correction framework for frozen vision-language-action (VLA) policies. It improves performance by aligning visual execution history with successful trajectories and transferring low-frequency residuals. RTCF achieves higher success rates in long-horizon manipulation tasks without requiring parameter updates or additional GPU resources.
From arXiv Robotics — research abstracts
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 · median latencies: 10.99 other · Basis not reported
“2,000 episodes per condition, RTCF raises aggregate success from 86.4% to 88.4% and improves LIBERO-Long from 61.6% to 68.6%.These gains require no parameter updates, repeated VLA inference, or additional GPU resources: correction can be performed on the client CPU after a single policy invocation, and the median latencies sum to only 10.99 ms per action chunk”
Exact source · revision 1Source owner not reported
Reported quantity · episodes per condition: 2000 other · Basis not reported
“2,000 episodes per condition, RTCF raises aggregate success from 86.4% to 88.4% and improves LIBERO-Long from 61.6% to 68.6%.These gains require no parameter updates, repeated VLA inference, or additional GPU resources: correction can be performed on the client CPU after a single policy invocation, and the median latencies sum to only 10.99 ms per action chunk”
Exact source · revision 1Source owner not reported
Evidence dependency changes (1)
- 2026-10-31T17:15:06.776Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalRetrieve in Time, Correct in Frequency
The paper presents RTCF, a training-free test-time correction framework for frozen vision-language-action (VLA) policies. It improves performance by aligning visual execution history with successful trajectories and transferring low-frequency residuals. RTCF achieves higher success rates in long-horizon manipulation tasks without requiring parameter updates or additional GPU resources.
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