Breaking the Average: Overcoming Convergent Defaults in Robotic Manipulation via Diagnostic Chain-of-Thought and Structured Guidance

Key points

• 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.1
• In this work, we propose a novel framework to identify and correct these behaviors without parameter updates.1
• 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.1
• We hypothesize that our method outperforms vanilla $π{0.5}$ and standard Chain-of-Thought prompting by effectively breaking convergent defaults in complex manipulation tasks.1

Sources (1)

• [1]Breaking the Average: Overcoming Convergent Defaults in Robotic Manipulation via Diagnostic Chain-of-Thought and Structured Guidance
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 08:52 AM

  Recent Vision-Language-Action (VLA) models, such as $π_{0.5}$, 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.

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