Sequential Recovery Updates for Learned Controllers under Successful-Trajectory Preservation
Overview
This research introduces a framework for improving learned controllers in humanoid robots by learning correction mechanisms without compromising existing successful behaviors. The method constrains entry rules rather than skill parameters, ensuring that updates only occur when the composed controller can reproduce new recovery strategies while exactly replaying all protected trajectories. Validated on two humanoid systems, the approach achieved 62 additional recoveries over 192 withheld perturbations without losing any of the base controller's successes. The study further analyzes the trade-offs involved in relaxing these constraints, demonstrating that removing them leads to failures in completing previously protected tasks.
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Event evidence and corrections
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Reported quantity · recoveries: 62 other · reported trials
“m 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.”
Exact source · revision 1Source owner not reported
Reported quantity · perturbations: 192 other · reported trials
“m 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.”
Exact source · revision 1Source owner not reported
Reported quantity · conditions: 33 other · reported trials
“m 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.”
Exact source · revision 1Source owner not reported
Reported quantity · protected completions lost: 8 other · reported trials
“m 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.”
Exact source · revision 1Source owner not reported
Reported quantity · recoveries gained by removing constraint: 33 other · reported trials
“m 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.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSequential Recovery Updates for Learned Controllers under Successful-Trajectory Preservation
This research introduces a framework for improving learned controllers in humanoid robots by learning correction mechanisms without compromising existing successful behaviors. The method constrains entry rules rather than skill parameters, ensuring that updates only occur when the composed controller can reproduce new recovery strategies while exactly replaying all protected trajectories. Validated on two humanoid systems, the approach achieved 62 additional recoveries over 192 withheld perturbations without losing any of the base controller's successes. The study further analyzes the trade-offs involved in relaxing these constraints, demonstrating that removing them leads to failures in completing previously protected tasks.
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