Abstract
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.
Abstract (translated)
对颗粒材料进行自动化操作面临着复杂的接触动力学、不可预测的材料特性和复杂系统状态等重大挑战。现有方法通常无法在这些任务中实现高效和准确的操作。为了填补这一研究空白，本文探讨了具有未知物理属性的小规模高精度颗粒材料挖掘任务，并提出了一种新的框架——可微挖土机器人（DDBot），用于操作包括沙子和土壤在内的颗粒材料。具体而言，我们为 DDBot 配备了一个基于物理学的可微模拟器，该模拟器专门针对颗粒材料的操作设计，采用 GPU 加速并行计算与自动微分技术。 通过这一框架，DDBot 能够进行有效的可微系统识别和未知颗粒材料的高精度挖掘技能优化。这得益于一种可微技能到动作映射、面向任务的演示方法、梯度裁剪以及基于线性搜索的梯度下降法。实验结果表明，DDBot 可以在 5 至 20 分钟内高效地识别未知颗粒材料的动力学并优化挖掘技能，在零样本真实世界部署中实现高精度的结果，突显了其实用性。对比最新的基准方法，DDBot 在此类挖掘任务中的鲁棒性和效率也得到了证实。
URL
https://arxiv.org/abs/2510.17335
