外观
论文HF Daily Papers · arXiv 2610.315696 天前
Echo框架：VLA模型耦合动作与状态信号去噪提升操作性能
本文由 AI 根据公开内容整理，可能有误，请以原文为准：Echo: Coupled Action and Task Status Signal Denoising for Vision Language Action Models（HF Daily Papers · arXiv 2610.31569）
核心内容
针对现有视觉语言动作（VLA）模型依赖外部监控或复杂架构判断任务完成与失败的痛点，研究提出Echo框架。该方法摒弃帧级进度标注，通过将任务状态信号内化为动作生成的组成部分，解决了细粒度监督缺失问题。真实世界操作实验表明，Echo能准确预测任务状态并一致性地提升策略性能，为具身智能体自主决策提供了轻量化新范式。
技术上，Echo将停止和失败信号作为两个额外维度添加至每个动作块中，与机器人动作联合去噪以区分执行、成功与失败状态。为解决无标注学习难题，系统构建包含成功、失败及恢复轨迹的互补数据集。关键创新在于控制信号与动作交互并采用差异化去噪速率，使状态信号先于动作收敛，确保后续动作生成步骤能基于更纯净的状态估计进行条件推理，实现状态引导的动作优化。
关键看点
- 信号嵌入机制：将停止与失败信号作为动作块的两个附加维度，与机器人动作在同一VLA模型内联合去噪，无需外部监测模块。
- 无标注学习策略：利用成功、失败及恢复轨迹数据的互补监督特性，摆脱对帧级任务进度标注的依赖，降低数据构建成本。
- 差异化去噪引导：设计信号与动作的受控交互，使状态信号去噪速率快于动作，让后续动作生成条件于更干净的状态估计。
- 实测性能验证：在真实世界操作任务中，Echo准确预测任务状态并持续提升策略表现，验证了状态信号直接指导动作生成的有效性。
工程与产业观察
Echo证明了在VLA架构内通过联合去噪机制融合离散状态信号与连续动作的可行性。其差异化去噪策略解决了多模态信号收敛不同步的工程难题，且无需昂贵帧级标注。这为具身大模型从单纯模仿学习向具备自主终止与恢复能力的闭环决策系统演进，提供了低数据成本的技术路径参考。
英文摘要 (Abstract)
Recognizing task completion and failure enables robots to stop upon success and initiate recovery or replanning after failure. Existing methods augment Vision Language Action (VLA) policies with task status signal prediction to support these execution decisions. However, learning and integrating these signals can require fine-grained supervision, external monitoring models, or complex architectural designs. Moreover, the potential of task status signals to directly guide action generation remains underexplored. We present Echo, a framework that combines task status signal learning without frame-level progress annotations with a compact design for status prediction and action generation within a single VLA. Specifically, Echo represents task status with stop and failure signals, added as two dimensions of each action chunk and denoised together with robot actions. Their combination distinguishes ongoing execution, successful completion, and failure. To learn these signals without fine-grained progress annotations, Echo constructs successful, failed, and recovery trajectories with complementary supervision properties. To enable status signal-guided action generation, Echo controls interactions between the signals and actions and denoises the signals faster than the actions. These allow later action denoising steps to condition on cleaner status estimates while preserving accurate signal prediction. Extensive real-world manipulation experiments show that Echo accurately predicts task status and consistently improves policy performance.
Hugging Face Daily Papers 热度：3 upvotes 作者：Yufeng Liu, Bohao Fan, Juntu Zhao, Xinyi Wang, Hang Yu, Wenzhuan Luo, Yingzhi Wang, Qinghai Li, Yingdong Hu, Junliang Guo, Richard Wang, Dequan Wang, Yang Gao, Cheng Ma
来源
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