Yang, Xintong  ORCID: https://orcid.org/0000-0002-7612-614X, Wei, Minglun, Lai, YuKun ORCID: https://orcid.org/0000-0002-2094-5680 and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902
2025.
DDBot: differentiable physics-based digging robot for unknown granular materials.
IEEE Transactions on Robotics
10.1109/TRO.2025.3636815
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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 key scientific problem addressed is the feasibility of applying first-order gradient- based optimisation to complex differentiable granular material simulation and overcoming associated numerical instability. 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.
| Item Type: | Article |
|---|---|
| Date Type: | Published Online |
| Status: | In Press |
| Schools: | Schools > Engineering |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 1552-3098 |
| Funders: | EPSRC |
| Date of First Compliant Deposit: | 8 December 2025 |
| Date of Acceptance: | 17 October 2025 |
| Last Modified: | 15 Dec 2025 11:15 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/182975 |
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