外观
论文arXiv 2610.1700116 天前
MOSAIC：模式和支持集感知的机器人扩散模型推理时控制
原文链接：MOSAIC: Mode- and Support-Aware Inference-Time Control of Robot Diffusion Models（arXiv 2610.17001 · 发布日期：2026-10-12）
MOSAIC: Mode- and Support-Aware Inference-Time Control of Robot Diffusion Models
MOSAIC以无梯度推理时控制调节冻结机器人扩散模型，并在导航、灵巧抓取、运动规划和协作搬箱中验证。
核心要点
- 机器人执行时常遇到训练外约束与目标，现有推理引导可能偏离先验、低效或模式坍塌。
- MOSAIC将初始噪声和反向步骤扰动视为联合输入，用完全去噪输出的权重自适应分布。
- 方法用自适应惩罚平衡物理可行性与学习支持集，并以按模式加权更新保留多种模式。
- 实验覆盖导航、灵巧抓取、含未知几何的运动规划和协作搬箱，显示无需重训即可适配和组合冻结先验。
验证范围与局限：验证范围限于摘要所列的导航、灵巧抓取、含未知几何的运动规划和协作搬箱任务。
英文摘要 (Abstract)
Generative policies such as diffusion models provide expressive data-driven priors for robot behavior, yet the situations a robot faces at execution time rarely match its training data and impose constraints and objectives the policy never learned. Existing inference-time guidance methods either rely on gradients that can push samples away from the learned prior, or on sampling procedures that can be inefficient, collapse modes, or average them into out-of-distribution predictions. We introduce MOSAIC, a mode- and support-aware framework for gradient-free inference-time control of robotic diffusion models. MOSAIC treats the initial noise and all stochastic reverse-step perturbations as a joint input to the pretrained denoising process and adapts a distribution over this input using importance weights computed from fully denoised outputs. Physical feasibility and consistency with the learned support are balanced using adaptive penalties, while mode-wise importance-weighted updates preserve modes and avoid destructive averaging. The same formulation can jointly steer multiple independently trained diffusion models through shared coupling costs, enabling coordinated behavior without requiring a joint model. We evaluate MOSAIC on navigation, dexterous grasping, motion planning with unseen geometry, and collaborative box manipulation with independently trained robot priors. Across these settings, MOSAIC adapts and composes frozen diffusion priors under new deployment constraints without retraining.
作者：Sabrina Bodmer, Rahel Rickenbach, René Zurbrügg, Melanie N. Zeilinger, Colin Jones
来源
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