aiqbench analysis
This paper tackles a specific problem in continual learning for robot controllers: adding new recovery skills to a deployed policy without breaking behaviours it already performs. Its main contribution is constraining when a new skill is allowed to activate, rather than freezing or retraining parameters. The reported gains, 62 new recoveries with no lost base successes on humanoid motions, come from a small set of streams and held-out perturbations, so readers should check how well they transfer to other platforms and disturbance distributions.
Sequential Recovery Updates for Learned Controllers under Successful-Trajectory Preservation
arXiv:2611.02555v1 Announce Type: cross Abstract: A competent learned controller already recovers from many disturbances. Improving it means learning a correction, and learning when not to apply it. Freezing a skill's parameters does not settle the second half. In a controller that routes among frozen skills, a new entry rule can redirect the states an old skill succeeded on. We therefore constrain entry instead of parameters. Each update searches for a feedback skill at a measured failure. It fits an entry rule that reads observations and rejects every state on an accumulated successful trajectory. The skill is admitted only if the composed controller reproduces the new recovery on its own and replays every protected trajectory exactly. A finite-trajectory condition states when this preserves the previous controller. One mechanism runs on four streams: two motions on each of two humanoid systems. Two updates per stream add 62 recoveries over 192 withheld perturbations, and lose none of the base controller's successes. We then price that guarantee. Removing the constraint recovers 33 more conditions but stops eight protected ones from completing at all, and excluding only what admission proves necessary recovers none of that cost.
Abstract:A competent learned controller already recovers from many disturbances. Improving it means learning a correction, and learning when not to apply it. Freezing a skill's parameters does not settle the second half. In a controller that routes among frozen skills, a new entry rule can redirect the states an old skill succeeded on. We therefore constrain entry instead of parameters. Each update searches for a feedback skill at a measured failure. It fits an entry rule that reads observations and rejects every state on an accumulated successful trajectory. The skill is admitted only if the composed controller reproduces the new recovery on its own and replays every protected trajectory exactly. A finite-trajectory condition states when this preserves the previous controller. One mechanism runs on four streams: two motions on each of two humanoid systems. Two updates per stream add 62 recoveries over 192 withheld perturbations, and lose none of the base controller's successes. We then price that guarantee. Removing the constraint recovers 33 more conditions but stops eight protected ones from completing at all, and excluding only what admission proves necessary recovers none of that cost.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2611.02555 [cs.RO]
(or arXiv:2611.02555v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.02555 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 282 words