PACER: Process-Aware VLA Policy Learning for Robotic Disassembly
Overview
PACER improves robotic disassembly by weighting valid action chunks based on progress. Achieves 87/100 success in motherboard disassembly, outperforming baseline methods.
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Reported quantity · successful approach-and-handoff trials: 87 other · Basis not reported · Differing source assertions
“and outcome to weight valid action chunks in the native supervised action loss. PACER trains one policy per weighting setting and selects one of the resulting policies through held-out validation. We evaluate PACER on UR5e motherboard disassembly across five component classes, where it achieves 87/100 successful approach-and-handoff trials on the held-out benchmark, compared with 54/100 for vanilla post-SFT.”
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Reported quantity · successful approach-and-handoff trials: 54 other · Basis not reported · Differing source assertions
“and outcome to weight valid action chunks in the native supervised action loss. PACER trains one policy per weighting setting and selects one of the resulting policies through held-out validation. We evaluate PACER on UR5e motherboard disassembly across five component classes, where it achieves 87/100 successful approach-and-handoff trials on the held-out benchmark, compared with 54/100 for vanilla post-SFT.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsPACER: Process-Aware VLA Policy Learning from Human Corrections for Robotic Disassembly
This paper presents PACER, a Process-Aware Correction and Evidence Reweighting framework for learning robotic disassembly policies from human corrections. The method improves policy performance by weighting valid action chunks based on progress toward the target and trace outcomes. Evaluated on UR5e motherboard disassembly, PACER achieves 87/100 successful approach-and-handoff trials, outperforming vanilla post-SFT methods with 54/100.
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