一句话能修复什么？冻结 VLA 策略的失败分类与最小语言修正
What Can One Sentence Fix? A Failure Taxonomy and a Minimal Language Correction for Frozen VLA Policies
AI 导读
一项 arXiv 研究对 OpenVLA-OFT 与 pi0.5 在 LIBERO-90 分布外任务上的失败进行分类，发现四类失败中只有“执行错位”能靠一句话修正：LLM 将该句转化为动作维度、方向、幅度和时间窗的偏置，作用于冻结策略。
正文 · 原文
Abstract:When a deployed vision-language-action (VLA) policy fails, fine-tuning it again for every new setting is costly, yet the person watching it can usually say what went wrong in one sentence. We ask what such a sentence can and cannot fix in a frozen policy. Labelling every failing LIBERO-90 task (out of distribution) for OpenVLA-OFT and pi0.5, we find that the failures sort into four modes and that only one, execution misalignment, where the policy reaches the right target but miscalibrates how far or how hard to move, is within reach of a sentence; the other three lack a target or a plan, not a magnitude. For this mode we define the narrowest human correction: one retrospective sentence per task, grounded by an LLM into an action dimension, direction, magnitude term, and time window, becomes a short bias on the frozen policy's actions, after which the policy completes the rest on its own, episode after episode. Re-executed on its task's failed rollouts, it recovers 65 of all 139 in-distribution and 33 of all 328 out-of-distribution failures in simulation; on a real xArm7 after the robot base is moved, it raises pick-and-place from 10/30 to 30/30 in each of two same-object conditions and from 2/30 to 24/30 with a new object, while two long-horizon tasks stay at 0/30 with and without correction, as the taxonomy predicts. A naively prompted VLM that watches the same failures and writes the same kind of correction recovers none in up to 10 attempts, suggesting that the value lies in the person's calibrated sense of magnitude and timing rather than in language itself. Videos: this https URL
| Comments: | CoRL 2026 Workshop on Learning from Corrections and Interventions. 8 pages, 6 figures. Project page: this https URL |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.29967 [cs.RO] |
| (or arXiv:2608.29967v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2608.29967 arXiv-issued DOI via DataCite |
Submission history
From: Owen Kwon [view email]
[v1]
Sun, 30 Aug 2026 18:54:03 UTC (4,410 KB)
[v2]
Fri, 23 Oct 2026 23:18:11 UTC (6,581 KB)
来源：arXiv cs.AI · arxiv.org