DDBot: Differentiable Physics-based Digging Robot for Unknown Granular Materials Article Swipe

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Xintong Yang , Max Wei , Yu‐Kun Lai , Ze Ji ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2510.17335

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.

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Type: preprint
Landing Page: http://arxiv.org/abs/2510.17335
PDF: https://arxiv.org/pdf/2510.17335
OA Status: green
OpenAlex ID: https://openalex.org/W4415901673

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OpenAlex ID: https://openalex.org/W4415901673

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DOI: https://doi.org/10.48550/arxiv.2510.17335

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Title: DDBot: Differentiable Physics-based Digging Robot for Unknown Granular Materials

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Type: preprint

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Publication year: 2025

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Publication date: 2025-10-20

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Authors: Xintong Yang, Max Wei, Yu‐Kun Lai, Ze Ji

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Landing page: https://arxiv.org/abs/2510.17335

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PDF URL: https://arxiv.org/pdf/2510.17335

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Cited by: 0

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