RePO-VLA: Recovery-Driven Policy Optimization for Vision-Language-Action Models
Vision-Language-Action (VLA) models remain brittle in long-horizon, contact-rich manipulation because success-only imitation provides little supervision for execution drift, while failed rollouts are often discarded. We introduce RePO-VLA, a recovery-driven policy optimization framework that assigns distinct roles to success, recovery, and failure trajectories. RePO-VLA first applies Recovery-Aware Initialization (RAI), slicing recovery segments and resetting history so corrective actions depend on the current adverse state rather than the preceding failure. It then learns a Progress-Aware Semantic Value Function (PAS-VF), aligning spatiotemporal trajectory features with instructions and ...
| Authors | Weijia Liufu, Xiaoyu Guo, Ruiyi Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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