English(EN) Ostrich: Taking Large Strides Through Stiff Contact in Differentiable Dynamics

新的Ostrich模拟器实现了更快、更节省内存的可微动力学

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了Ostrich，一种新颖的GPU加速刚体模拟器，专为可微动力学设计。Ostrich通过在显著更大的时间步长内实现接触的基于梯度的优化，解决了现有模拟器的局限性。它使用非光滑牛顿迭代并通过隐函数定理区分收敛残差来实现这一点，每个时间步的内存使用量是恒定的。 AI

影响这个新的模拟器可以通过接触实现更高效的基于梯度的优化，从而加速机器人学和强化学习的研究与开发。

排序理由该集群包含一篇详细介绍新模拟器的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Ale\v{s} Ku\v{c}era, Karel Zimmermann · 2026-09-10 04:00

Ostrich：在可微分动力学中，通过刚性接触迈出大步

arXiv:2609.08800v1 Announce Type: cross Abstract: Three properties determine whether a differentiable simulator can drive gradient-based optimization through contact: simulation accuracy, gradient reliability, and per-iteration cost. Tape-based engines such as MJX and Newton Semi…