Steer2Grasp: 多様な物理的に実行可能な把持拡散のための推論時身体性考慮ステアリング
Steer2Grasp: Inference-Time Embodiment-Aware Steering for Diverse Physically Feasible Grasp Diffusion
凍結した把持拡散モデルを展開先の報酬で推論時に誘導し、実行不可能な把持モードから高報酬モードへ集団レベルで遷移させる学習不要の枠組みを提案。
著者: Vignesh Vembar, Ayush Kaura, A Padmaprabhan, Siddharth Sinha, Kailash Nagarajan, Keshab Patra, Md Faizal Karim, K Madhava Krishna
分類: cs.RO
原文アブストラクト
Current grasp diffusion models provide rich priors for generation, yet their object-centric approach can violate the kinematic and collision constraints imposed by the embodiment and the environment. Existing embodiment-aware methods primarily perform local corrections around generated grasps through gradient guidance or optimization, making it difficult to recover from fundamentally infeasible modes. We present Steer2Grasp, a training-free, embodiment-agnostic framework for inference-time grasp steering that adapts a frozen Cartesian grasp diffusion model using deployment-specific rewards. Through Feynman-Kac (FK) inspired particle reweighting and resampling, the method reallocates population mass from infeasible to high-reward grasp modes, enabling population-level mode transitions without modifying the pretrained diffusion model or requiring differentiable constraints. The framework enables a unified treatment for single and dual arm grasping through reachability and collision aware rewards, followed by gradient free gripper level local refinement. Across diverse objects, robot embodiments, and constrained environments, our method substantially improves feasible grasp generation while maintaining proximity to the underlying grasp prior.