滑らかに微分可能で効率的にベクトル化可能な接触マニフォールド生成
Smoothly Differentiable and Efficiently Vectorizable Contact Manifold Generation
ロボティクス向けに、微分可能かつベクトル化可能な接触マニフォールド生成フレームワークを提案し、Mujoco XLAと比較して大幅な高速化を実現した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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4. どうやって有効だと検証した？
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著者: Onur Beker, Andreas René Geist, Anselm Paulus, Nico Gürtler, Ji Shi, Sylvain Calinon, Georg Martius
分類: cs.RO
原文アブストラクト
Simulating rigid-body dynamics with contact in a fast, massively vectorizable, and smoothly differentiable manner is highly desirable in robotics. An important bottleneck faced by existing differentiable simulation frameworks is contact manifold generation: representing the volume of intersection between two colliding geometries via a discrete set of properly distributed contact points. A major factor contributing to this bottleneck is that the related routines of commonly used robotics simulators were not designed with vectorization and differentiability as a primary concern, and thus rely on logic and control flow that hinder these goals. We instead propose a framework designed from the ground up with these goals in mind, by trying to strike a middle ground between: i) convex primitive based approaches used by common robotics simulators (efficient but not differentiable), and ii) mollified vertex-face and edge-edge unsigned distance-based approaches used by barrier methods (differentiable but inefficient). Concretely, we propose: i) a representative set of smooth analytical signed distance primitives to implement vertex-face collisions, and ii) a novel differentiable edge-edge collision routine that can provide signed distances and signed contact normals. The proposed framework is evaluated via a set of didactic experiments and benchmarked against the collision detection routine of the well-established Mujoco XLA framework, where we observe a significant speedup. Supplementary videos can be found at https://github.com/bekeronur/contax, where a reference implementation in JAX will also be made available at the conclusion of the review process.
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