发表机构
Cardiff University; Hohai University（卡迪夫大学; 河海大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过MPM系统辨识基准，揭示可微物理优化在机器人材料操控中因GPU求和顺序、有限差分不可靠及目标定义差异导致的数值问题，并提出可复现累加与验证建议。
AI 中文摘要
可微物理日益被用于机器人材料操控中的系统辨识、轨迹或技能优化、演示生成以及机器人或末端执行器设计。这些应用依赖于通过长时程、接触密集的模拟推演所传播的梯度。我们利用两个源自弹塑性和颗粒状操控的材质点法（MPM）系统辨识基准，研究了这些梯度的数值可靠性。这些基准为三种在更广泛的可微物理优化中也会出现的效应提供了受控案例。GPU上的多对一求和，其顺序取决于线程调度，改变了长时程梯度，并使一个参数梯度的符号相对于确定性参考发生了反转。有限差分检验在更长的推演中变得不那么可靠，因为重复运行损失的变化远快于所测试参数扰动引起的损失变化。观测和损失的定义改变了优化行为以及独立度量所偏好的解。这些结果促使采用可复现的累加方式、将扰动引起的损失变化与重复运行变化进行比较的有限差分验证，以及在可微模拟用于机器人优化时明确报告目标构建。
英文摘要
Differentiable physics is increasingly used in robotic material manipulation for system identification, trajectory or skill optimization, demonstration generation, and robot or end-effector design. These applications depend on gradients propagated through long, contact-rich simulation rollouts. We study the numerical reliability of those gradients using two Material Point Method (MPM) system-identification benchmarks derived from elastoplastic and granular manipulation. The benchmarks provide controlled cases for three effects that also arise in broader differentiable physics-based optimization. GPU many-to-one sums whose order depends on thread scheduling changed long-horizon gradients and reversed the sign of one parameter gradient relative to a deterministic reference. Finite-difference checks became less reliable for longer rollouts because repeated-run loss variation grew much faster than the loss change produced by the tested parameter perturbations. Observation and loss definitions changed optimization behaviour and the solution preferred by an independent metric. These results motivate reproducible accumulation, finite-difference validation that compares perturbation-induced loss changes with repeated-run variation, and explicit reporting of objective construction when differentiable simulation is used for robotic optimization.
CommentsAccepted as an oral presentation at the IROS 2026 workshop: Deformable Objects Manipulation: Research Foundations, Reproducibility and Real-World Challenges
