The paper investigates how vision–language models (VLMs) diagnose failures in bimanual tool‑use tasks, where failures can arise from tool slip, timing mismatches between hands, or combined causes. It evaluates which sensing channels enable a diagnoser to identify each failure type, finding that tool slip is detectable on the tool hand, timing delays are observable on both arms, and some combined failures remain indistinguishable from passive sensing. The study also shows that a repair policy that escalates when the cause is unclear can match the performance of a policy with full cause knowledge, and that a VLM’s internal state captures the late hand almost perfectly, though the answer depends on question wording.
arXiv:2606. 03134v1 Announce Type: cross Abstract: Imitation-learning policies for robot manipulation inherit the quality of the success labels attached to their training episodes, and those labels are usually produced by the robot's own success check.
By Aarav Bedi (University of California, Berkeley)
arXiv:2609.21942v1 Announce Type: cross
Abstract: A robot that fails at a task faces the first decision in corrective dialogue: act on its own diagnosis, consult another onboard sensor, or interrupt...
By Eshika Pathak, Leela Krishna
arXiv:2610.32941v1 Announce Type: cross
Abstract: Vision-Language-Action (VLA) models perform well on robotic manipulation, yet remain brittle once their own actions carry them into states their trai...
By Thomas Landeg, Thierry Tambe
The study investigates how language models equipped with tools can still produce unsupported final claims, even when a single tool call could resolve the uncertainty. It defines two metrics—occurrence (how often unsupported claims arise) and conditional repair (how often they are fixed when evidence is provided). Experiments on Qwen3-32B and Gemma 4 show that providing the missing evidence consistently repairs all unsupported claims in the Qwen3-32B setup, while the Gemma 4 model never produced unsupported claims under the tested conditions.
By Justin Bronder
The study investigates why small language model agents tend to repeat a tool call that just failed. By recording the failed call and its error message in the transcript, the authors measure a negative corrective gain—agents are more likely to repeat the failed action, with a drop of about 1.03 nats per token. The problem is traced to the harness design rather than the model’s understanding of errors, and the authors show that replacing the verbatim call with a runtime-generated description of the failure can reduce this backfiring effect by 76%.
By Esmail Gumaan
The paper investigates whether a single descriptive sentence can correct failures in frozen vision‑language‑action (VLA) policies. By labeling failures in the LIBERO‑90 benchmark, the authors identify four failure modes, finding that only execution misalignment—where the policy reaches the correct target but misjudges distance or force—can be remedied with a concise human‑generated sentence. This sentence, parsed by an LLM into a bias on the policy’s actions, successfully restores performance on both simulated and real‑world tasks, while automated language models fail to achieve similar gains.
By Owen Kwon, Pablo Ortega-Kral, Arthur Bucker, Jean Oh
arXiv:2606. 08542v1 Announce Type: cross Abstract: Exploratory manipulation often turns an apparent failed attempt into the key evidence for what to do next.
By Haizhou Ge, Yufei Jia, Yue Li, Zhixing Chen, Lu Shi, Lei Han, Guyue Zhou, Ruqi Huang
arXiv:2610.10388v1 Announce Type: cross
Abstract: Recent advances in multimodal foundation models have made them capable generalist physical agents for a range of manipulation tasks. However, success...
By Liu Renhang, Navonil Majumder, Tej Deep Pala, Soujanya Poria
arXiv:2610.41025v1 Announce Type: new
Abstract: When a language-model agent fails a multi-step task, can another model find the decisive mistake and fix it with a single action? We introduce ReplayRC...
By Jeremy Canale, Fabien Soulis
arXiv:2609.36808v1 Announce Type: cross
Abstract: Current embodied models do not respond to their own failures, although what just went wrong could inform a small adjustment on the next attempt, the...
By Long Li, Qichao Zhao, Yue Yang, Fan Xu, Zhe Wang, Alan Wee-Chung Liew, Chao Qu, Heng Tao Shen, Shirui Pan
The paper investigates Observation‑Space Shift (OSS), a failure mode in long‑horizon robotic manipulation where downstream skills start from states altered by preceding skills. By diagnosing that displaced scene elements—not robot joints or target objects—cause most OSS, the authors develop a detect‑restore‑resume system that monitors task progress, restores the scene, and fine‑tunes the skill to resume. On the BOSS‑44 benchmark, this approach raises full‑chain success from 7.6 % to 26.5 %, outperforming several baselines and demonstrating the value of scene restoration before policy resumption.
By Pranav Wagh, Yu Fang, Yue Yang, Mingyu Ding