让两个动作头达成一致：流匹配策略的协调机制与运行时崩溃证书

中文摘要 AI 辅助

双表示流匹配策略将每个预测运动解码为关节空间和末端执行器空间，两种运动学等效解码之间的残差提供了可物理解释的运行时信号。然而在多模态任务中，独立采样的分支可能选择不同的有效模态，从而引发误报。我们研究如何协调两个分支及其代价，在两个机器人环境和一个非机器人测试台上，所测试的机制分为四类：两个分支共享但流匹配构造中不存在的辅助潜在变量，在种群最优时会被消除，这是在指定的2%等效带内确认的可证明死胡同；共享源噪声可实现协调或反协调，其效果随表示图改变符号并跟踪解码器模态盆地的对齐情况；一致性正则化提供中等程度的协调，但会降低有效对的比例；训练支持的离散划分可稳健实现接近上限的协调。我们进一步仅基于每个分支的基尼-辛普森多样性推导了机会校正的协调边界，得到了可达区域和无标签证书，用于在零歧义错配时区分协调与崩溃。在LIBERO-Plus上，良性多模态会使残差的误报率增加1.57个百分点，残差仍是评估中最强的故障信号；预注册的令牌干预未达到其误报标准，也未产生种子鲁棒的检测变化。代码、模型和每次运行的配置可在该https URL获取。

英文摘要

A dual-representation flow-matching policy decodes each predicted motion into joint and end-effector spaces, and the residual between the two kinematically equivalent decodings provides a physically interpretable runtime signal. On multimodal tasks, however, independently sampled branches may choose different valid modes, causing false alarms. We study how to coordinate the two branches and at what cost. Across two robot environments and a non-robotic testbed, the tested mechanisms fall into four classes. An auxiliary latent shared by both branches but absent from the flow-matching construction is erased at the population optimum, a provable dead end confirmed within a prespecified 2% equivalence band. Sharing source noise can coordinate or anti-coordinate: its effect changes sign with the representation map and tracks the alignment of decoder mode basins. Consistency regularization gives intermediate coordination but reduces the valid-pair rate, while training-supported discrete partitions achieve near-ceiling coordination robustly. We further derive a chance-corrected coordination bound based only on each branch's Gini-Simpson diversity, yielding an attainable region and a label-free certificate that separates coordination from collapse when zero mismatch is ambiguous. On LIBERO-Plus, benign multimodality adds 1.57 percentage points of false alarms to the residual, which remains the strongest evaluated failure signal; the preregistered token intervention does not meet its false-alarm criterion or produce a seed-robust detection change. Code, models, and per-run configurations are available at https://github.com/kimo423/dual-head-coordination.

发表机构

• School of Automation, Nanjing University of Science and Technology（南京理工大学自动化学院）

机构由 AI 辅助整理，请以论文原文为准。