基于可微模拟的几何合理物体姿态细化
Geometrically Plausible Object Pose Refinement using Differentiable Simulation
AI总结：
本文提出结合可微物理模拟、可微渲染和视觉-触觉传感的多模态姿态优化方法，提升空间准确性和物理一致性，实验表明在初始估计准确和高初始不确定性下，显著优于标准ICP基线。
摘要：
最先进的物体姿态估计方法容易生成几何上不可行的姿态假设。在灵巧操作中，估计的姿态常与机械手相交或不位于支撑表面。我们提出一种多模态姿态细化方法，结合可微物理模拟、可微渲染和视觉-触觉传感，以优化物体姿态的空间准确性和物理一致性。模拟实验表明，当初始估计准确时，我们的方法将物体与机械手的相交体积误差减少了73%，在高初始不确定性下减少超过87%，显著优于标准ICP基线。此外，几何合理性提升的同时，平移和方向误差也同时减少。实现基于物理现实的姿态估计，同时保持对多模态传感器输入的忠实，是实现稳健手部操作的关键步骤。
英文摘要：
State-of-the-art object pose estimation methods are prone to generating geometrically infeasible pose hypotheses. This problem is prevalent in dexterous manipulation, where estimated poses often intersect with the robotic hand or are not lying on a support surface. We propose a multi-modal pose refinement approach that combines differentiable physics simulation, differentiable rendering and visuo-tactile sensing to optimize object poses for both spatial accuracy and physical consistency. Simulated experiments show that our approach reduces the intersection volume error between the object and robotic hand by 73\% when the initial estimate is accurate and by over 87\% under high initial uncertainty, significantly outperforming standard ICP-based baselines. Furthermore, the improvement in geometric plausibility is accompanied by a concurrent reduction in translation and orientation errors. Achieving pose estimation that is grounded in physical reality while remaining faithful to multi-modal sensor inputs is a critical step toward robust in-hand manipulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
