PACER: Process-Aware VLA Policy Learning from Human Corrections for Robotic Disassembly

Chang Liu, Sibo Tian, Litian Gong, Zhiwen Fan, Jiachen Li, Xiao Liang, Minghui Zheng · N/A · 2026

Summary

Robotic disassembly of end-of-life (EoL) products requires robots to accommodate product variation, follow prerequisite sequences, and approach components precisely for contact-rich manipulation.

Abstract Summary

Robotic disassembly of end-of-life (EoL) products requires robots to accommodate product variation, follow prerequisite sequences, and approach components precisely for contact-rich manipulation. Supervised fine-tuning (SFT) provides a practical path for adaptation, but limited and costly disassembly data cannot capture the distribution shifts arising from changing environments and product uncertainties. The policy's own rollouts record its approach attempts, and human corrections demonstrate recovery from its errors, but turning these experiences into better policy performance remains challenging. We introduce PACER, a Process-Aware Correction and Evidence Reweighting framework that learns from these experiences through SFT, using progress toward the target and each trace's source 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.

Key Points

Robotic disassembly of end-of-life (EoL) products requires robots to accommodate product variation, follow prerequisite sequences, and approach components precisely for contact-rich manipulation.
Supervised fine-tuning (SFT) provides a practical path for adaptation, but limited and costly disassembly data cannot capture the distribution shifts arising from changing environments and product uncertainties.
The policy's own rollouts record its approach attempts, and human corrections demonstrate recovery from its errors, but turning these experiences into better policy performance remains challenging.
We introduce PACER, a Process-Aware Correction and Evidence Reweighting framework that learns from these experiences through SFT, using progress toward the target and each trace's source 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.

PACER

We introduce PACER, a process-aware correction and evidence-reweighting framework for VLA post-training from heterogeneous disassembly traces, as shown in Figure 2 . PACER operates on the data side and preserves the action representation, the VLA backbone architecture, and the native per-sample action prediction objective, modifying only the sample-level contribution of each training row to that objective.

Experiments

Setup. We evaluate PACER on a UR5e motherboard-disassembly platform across five components: CPU, GPU, CPU fan, RAM, and the connector block. The supervised reference policy $\pi_{\theta_{0}}$ is obtained by supervised fine-tuning of $\pi_{0.5}$-DROID on an initial set $\mathcal{D}_{\mathrm{demo}}$ of 710 teleoperated trajectories.

RAM and connector-block demonstrations are split across multiple hardware configurations, yielding lower per-configuration coverage than the three single-configuration components.

Experiments — PACER: Process-Aware VLA Policy Learning from Human Corrections for Robotic Disassembly — Figure 2: PACER converts autonomous rollouts, operator corrections, and supplementary clean demonstrations into a weighted native-loss training signal by checking imitation eligibility and weighting valid action chunks with process evidence.

Ablation Study

To validate the effectiveness of PACER, we assess the importance of supplementary clean demonstrations, human corrections, and autonomous rollouts for PACER post-training by removing each source in turn. We also compare selected PACER with four fixed weighting alternatives, as shown in Table 2 . Each of the eight policies in this comparison is trained from $\pi_{\theta_{0}}$ with the same nominal training budget.

Sources and Demonstrations

Figures are reproduced from Liu et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.