微分可能シミュレーションを用いた幾何学的に妥当な物体姿勢精緻化
Geometrically Plausible Object Pose Refinement using Differentiable Simulation
物体姿勢推定の結果が幾何学的に不自然になる問題を解決するため、微分可能な物理シミュレーションとレンダリング、視覚・触覚センサを組み合わせて姿勢を最適化する手法を提案した。
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著者: Anil Zeybek, Rhys Newbury, Snehal Dikhale, Nawid Jamali, Soshi Iba, Akansel Cosgun
分類: cs.RO
原文アブストラクト
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.
