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

TL;DR

DDBot introduces a differentiable physics-based framework enabling high-precision, efficient manipulation of unknown granular materials like sand and soil through gradient-based optimization and GPU acceleration.

Contribution

The paper presents a novel differentiable digging robot framework that integrates a GPU-accelerated granular material simulator for system identification and skill optimization.

Findings

DDBot converges within 5-20 minutes for unknown material dynamics.

Achieves high-precision digging in zero-shot real-world tests.

Outperforms state-of-the-art baselines in robustness and efficiency.

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 article 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 optimization 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 optimization 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 optimize 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.