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.

翻译：机器人自动化通过减少实验室工作流程中的人工操作，正在加速科学发现。然而，粉末的精确操作仍然具有挑战性，尤其是在运输等需要高精度和稳定性的任务中。我们提出了一种用于实验室环境中粉末运输的轨迹优化框架，该框架集成了可微分物理模拟以精确建模颗粒动力学，采用低维技能空间参数化以降低优化复杂度，并采用基于课程学习的策略，在长时域内逐步提升任务执行能力。该框架实现了接触密集型机器人轨迹的端到端优化，同时保持了稳定性和收敛效率。实验结果表明，与强化学习基线相比，所提方法在任务成功率和稳定性方面均表现出更优的性能。