同一动作用多坐标系协同去噪，提升双臂机器人操作精度。
Mixture of Frames Policy: Multi-Frame Action Denoising for Bimanual Mobile Manipulation
- 用多个任务相关坐标系并行去噪，再融合回主坐标系。
- 九个模拟任务中优于单帧基准和最优帧选择方案。
- 适合复杂双臂协作任务，实测在真实场景也更可靠。
机器人操作本质上是多帧的：末端执行器帧中的局部动作可能简单，而搬运、直立物体处理及全身协调更适合基底对齐帧表示。然而，现代基于扩散的视觉-运动策略通常固定使用单一预定义动作帧，迫使一个去噪器建模在该帧中本不必要的复杂动作分布。我们提出混合帧策略（MoF），一种在多个坐标系间同步进行动作去噪的扩散策略。MoF维护单一标准扩散状态，将其重表达为多个任务相关帧，应用各帧专用的去噪器，并将噪声预测融合回标准帧。为使中间噪声状态也可实现此操作，我们在SE(3)动作参数化中引入列式6D旋转表示，支持精确、可微的帧变换，无需噪声旋转位于SO(3)流形上。在九个模拟双臂操作任务中，我们证明最佳动作帧具有任务依赖性，且MoF优于最优帧选择和标准混合专家（MoE）基线。进一步在两个真实世界双臂移动操作任务上评估，显示其性能超过所有单帧基线。
原文摘要 · Abstract (English)
Robotic manipulation is inherently multi-frame: local actions may be simple in an end-effector frame, while transport, upright-object handling, and whole-body coordination are better represented in a base-aligned frame. However, modern diffusion-based visuomotor policies typically commit to a single predefined action frame, forcing one denoiser to model action distributions that are often unnecessarily complex in that frame. We propose Mixture of Frames Policy (MoF), a diffusion policy that performs synchronized action denoising across multiple coordinate frames. MoF maintains a single canonical diffusion state, re-expresses it in several task-relevant frames, applies frame-specialized denoisers, and fuses their noise predictions back in the canonical frame. To make this possible for intermediate noisy diffusion states, we introduce a column-based 6D rotation representation within an SE(3) action parameterization that supports exact, differentiable frame transformations without requiring noisy rotations to lie on the SO(3) manifold. Across nine simulated bimanual manipulation tasks, we show that the best action frame is task-dependent and that MoF improves over oracle frame selection and standard Mixture-of-Experts (MoE) baselines. We further evaluate MoF on two real-world bimanual mobile manipulation tasks, demonstrating that it outperforms all constituent single-frame baselines. Project homepage: https://mofpo.github.io
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