A Physics-informed Demonstration-guided Learning Framework for Granular Material Manipulation

TL;DR

This paper introduces a physics-informed, differentiable simulator and demonstration-guided learning framework for granular material manipulation, enabling robust robotic policies in simulation and real-world settings.

Contribution

It presents a novel differentiable physics-based simulator for granular materials and a demonstration-guided learning approach that reduces data requirements and improves policy robustness.

Findings

Robust policies successfully manipulate granular materials in simulation and real-world.

The method outperforms standard reinforcement and imitation learning approaches.

The framework reduces data collection and training costs.

Abstract

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.