aiqbench analysis
The paper proposes a prompting-based method, with no parameter updates, for correcting "mode averaging" in vision-language-action robot policies like π0.5. It is validated only in ManiSkill3 simulation, and the authors frame their central claim as a hypothesis, so real-world robustness remains untested. Readers interested in embodied AI should watch whether the "Lens Prompt" approach generalizes beyond simulation and beyond the chosen manipulation tasks.
Breaking the Average: Overcoming Convergent Defaults in Robotic Manipulation via Diagnostic Chain-of-Thought and Structured Guidance
arXiv:2610.17983v1 Announce Type: new Abstract: Recent Vision-Language-Action (VLA) models, such as $\pi_{0.5}$, have demonstrated impressive generalization capabilities by scaling behavior cloning across massive datasets. However, these models frequently suffer from distributional convergence, a phenomenon where the policy regresses to "safe" but suboptimal mean behaviors due to mode averaging in the training data. This often results in generic grasp strategies, Convergent Defaults, that fail in constrained or long-tail scenarios. In this work, we propose a novel framework to identify and correct these behaviors without parameter updates. We introduce a three-stage methodology: (1) Probing for Defaults, identifying task-specific mode collapse; (2) Diagnostic Root Cause Analysis, utilizing the VLA's latent reasoning capabilities to verbalize the logic behind suboptimal actions; and (3) Structured Guidance Injection, where concrete, distinct alternatives are encoded into reusable "Lens Prompts" that shift the sampling distribution. We validate this approach using $\pi_{0.5}$ in ManiSkill3 simulation environments. We hypothesize that our method outperforms vanilla $\pi_{0.5}$ and standard Chain-of-Thought prompting by effectively breaking convergent defaults in complex manipulation tasks.
Abstract:Recent Vision-Language-Action (VLA) models, such as $\pi_{0.5}$, have demonstrated impressive generalization capabilities by scaling behavior cloning across massive datasets. However, these models frequently suffer from distributional convergence, a phenomenon where the policy regresses to "safe" but suboptimal mean behaviors due to mode averaging in the training data. This often results in generic grasp strategies, Convergent Defaults, that fail in constrained or long-tail scenarios. In this work, we propose a novel framework to identify and correct these behaviors without parameter updates. We introduce a three-stage methodology: (1) Probing for Defaults, identifying task-specific mode collapse; (2) Diagnostic Root Cause Analysis, utilizing the VLA's latent reasoning capabilities to verbalize the logic behind suboptimal actions; and (3) Structured Guidance Injection, where concrete, distinct alternatives are encoded into reusable "Lens Prompts" that shift the sampling distribution. We validate this approach using $\pi_{0.5}$ in ManiSkill3 simulation environments. We hypothesize that our method outperforms vanilla $\pi_{0.5}$ and standard Chain-of-Thought prompting by effectively breaking convergent defaults in complex manipulation tasks.
Robotics (cs.RO)
arXiv:2610.17983 [cs.RO]
(or arXiv:2610.17983v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.17983 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 255 words
