2024/06/13 by Minglun Wei, Xintong Yang, Wei, Minglun +7 · 2 citations
Computer Science · Engineering · #FOS: Computer and information sciences #Image Processing and 3D Reconstruction #Robot Manipulation and Learning #Robotics (cs.RO) #Tunneling and Rock Mechanics
paper · pdf · doi:10.48550/arxiv.2406.09178
openalex publication_date 2024/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Due to the complex physical properties of granular materials, research on robot learning for manipulating such materials predominantly either disregards the consideration of their physical characteristics or uses surrogate models to approximate their physical properties. Learning to manipulate granular materials based on physical information obtained through precise modelling remains an unsolved problem. In this paper, we propose to address this challenge by constructing a differentiable physics-based simulator for granular materials using the Taichi programming language and developing a learning framework accelerated by demonstrations generated through gradient-based optimisation on non-granular materials within our simulator, eliminating the costly data collection and model training of prior methods. Experimental results show that our method, with its flexible design, trains robust policies that are capable of executing the task of transporting granular materials in both simulated and real-world environments, beyond the capabilities of standard reinforcement learning, imitation learning, and prior task-specific granular manipulation methods.