  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 simulator for granular
materials based on the Taichi programming language and developing a learning
framework accelerated by imperfect demonstrations that are generated via
gradient-based optimisation on non-granular materials through our simulator.
Experimental results show that our method trains three policies that, when
chained, are capable of executing the task of transporting granular materials
in both simulated and real-world scenarios, which existing popular deep
reinforcement learning models fail to accomplish.
