MOSAIC: モード・サポート考慮型ロボット拡散モデルの推論時制御
MOSAIC: Mode- and Support-Aware Inference-Time Control of Robot Diffusion Models
学習済み拡散モデル方策を凍結したまま、推論時に勾配なしで制御し、未見の制約や複数モデルの協調動作に対応させる手法を提案。
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著者: Sabrina Bodmer, Rahel Rickenbach, René Zurbrügg, Melanie N. Zeilinger, Colin Jones
分類: cs.RO
原文アブストラクト
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.
関連論文
- 自己誘導型アクション拡散拡散方策
