Automating the manipulation of granular materials poses significant challenges due to complex contact dynamics, unpredictable material properties, and intricate system states. Existing approaches often fail to achieve efficiency and accuracy in such tasks. To fill the research gap, this paper studies the small-scale and high-precision granular material digging task with unknown physical properties. A new framework, named differentiable digging robot (DDBot), is proposed to manipulate granular materials, including sand and soil. Specifically, we equip DDBot with a differentiable physics-based simulator, tailored for granular material manipulation, powered by GPU-accelerated parallel computing and automatic differentiation. DDBot can perform efficient differentiable system identification and high-precision digging skill optimisation for unknown granular materials, which is enabled by a differentiable skill-to-action mapping, a task-oriented demonstration method, gradient clipping and line search-based gradient descent. Experimental results show that DDBot can efficiently (converge within 5 to 20 minutes) identify unknown granular material dynamics and optimise digging skills, with high-precision results in zero-shot real-world deployments, highlighting its practicality. Benchmark results against state-of-the-art baselines also confirm the robustness and efficiency of DDBot in such digging tasks.

翻译：自动化操控颗粒材料因其复杂的接触动力学、不可预测的材料特性以及错综的系统状态而面临重大挑战。现有方法在此类任务中往往难以兼顾效率与精度。为填补这一研究空白，本文研究了物理属性未知情况下的小规模、高精度颗粒材料挖掘任务。我们提出了一种名为可微分挖掘机器人（DDBot）的新框架，用于操控包括沙土在内的颗粒材料。具体而言，我们为DDBot配备了专为颗粒材料操控设计的可微分物理模拟器，该模拟器由GPU加速并行计算与自动微分技术驱动。通过可微分技能-动作映射、面向任务的演示方法、梯度裁剪及基于线搜索的梯度下降，DDBot能够对未知颗粒材料执行高效的可微分系统辨识与高精度挖掘技能优化。实验结果表明，DDBot能高效（5至20分钟内收敛）辨识未知颗粒材料动力学并优化挖掘技能，在零样本现实场景部署中取得高精度结果，凸显其实用性。与前沿基准方法的对比结果也证实了DDBot在此类挖掘任务中的鲁棒性与高效性。