Robot disassembly learns from its own rollouts: PACER turns autonomous traces and human corrections into training weights, reaching 87/100 motherboard handoffs
In electronic-waste disassembly, demonstrations for vision-language-action (VLA) policies are scarce, and supervised fine-tuning alone struggles to cover the state shifts a deployed policy meets. PACER compiles autonomous rollouts, operator corrections and supplementary clean demonstrations into typed action-chunk rows, then weights each row by progress toward the declared target and by the trace's source and role, while keeping the native supervised action loss. On held-out hardware for UR5e motherboard disassembly across five components, the selected policy completed 87/100 approach-and-handoff trials, against 54/100 for uniformly weighted post-SFT and 79/100 for fixed-geometry reweighting.