REDIRECT: A 1% Fix for Bad Robot Habits
Overview
A new method called REDIRECT addresses robot habit issues by fixing local differences with just 1% of training data, improving teleoperation efficiency.
Generated from attributed reports · 1 hours agoUpdated
Event evidence and corrections
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Reported quantity · Reported success rate: 68.2 percent · Basis not reported · Differing source assertions
“s the mean success rate from 68.2% to 90.3%, recovering 88.8% of the clean-retraining gap versus 22.1% for matched-compute fine-tuning. On a PiPER arm, it recovers 86.7-92.9% of the clean-only gap across cup insertion and towel folding. Local robot habits can therefore be repaired by spending the update budget on the behavioral difference rather than relearning shared behavior.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 90.3 percent · Basis not reported · Differing source assertions
“s the mean success rate from 68.2% to 90.3%, recovering 88.8% of the clean-retraining gap versus 22.1% for matched-compute fine-tuning. On a PiPER arm, it recovers 86.7-92.9% of the clean-only gap across cup insertion and towel folding. Local robot habits can therefore be repaired by spending the update budget on the behavioral difference rather than relearning shared behavior.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsREDIRECT: A 1% Fix for Bad Robot Habits
Robots can develop bad habits from a few defective moments in otherwise useful teleoperation. This paper introduces REDIRECT, a method that fixes these local differences using only 1% of the full-training sample budget. By localizing problematic behavior and assigning coherent continuations, REDIRECT improves success rates from 68.2% to 90.3% across three ManiSkill tasks and recovers most of the gap compared to clean-retraining.
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