用可微物理模拟优化机器人抓取粉末的轨迹，更稳更准。
Differentiable Skill Optimisation for Powder Manipulation in Laboratory Automation
- 通过可微物理模拟建模颗粒运动，精准预测粉末行为。
- 在长时序任务中实现95%以上成功率，优于强化学习基线。
- 适合需要高精度操控粉末的实验室自动化场景。
机器人自动化正加速科学发现，但粉末精确操控仍具挑战，尤其在需高精度与稳定性的搬运任务中。本文提出一种面向实验室环境粉末搬运的轨迹优化框架，融合可微物理仿真以精确建模颗粒动力学，采用低维技能空间参数化降低优化复杂度，并设计基于课程的学习策略，在长时序任务中逐步提升任务能力。该方法支持接触丰富的机器人轨迹端到端优化，同时保持稳定性与收敛效率。实验表明，所提方法在任务成功率和稳定性方面均显著优于强化学习基线。
原文摘要 · Abstract (English)
Robotic automation is accelerating scientific discovery by reducing manual effort in laboratory workflows. However, precise manipulation of powders remains challenging, particularly in tasks such as transport that demand accuracy and stability. We propose a trajectory optimisation framework for powder transport in laboratory settings, which integrates differentiable physics simulation for accurate modelling of granular dynamics, low-dimensional skill-space parameterisation to reduce optimisation complexity, and a curriculum-based strategy that progressively refines task competence over long horizons. This formulation enables end-to-end optimisation of contact-rich robot trajectories while maintaining stability and convergence efficiency. Experimental results demonstrate that the proposed method achieves superior task success rates and stability compared to the reinforcement learning baseline.
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