  Legged robot locomotion on sand slopes is challenging due to the complex
dynamics of granular media and how the lack of solid surfaces can hinder
locomotion. A promising strategy, inspired by ghost crabs and other organisms
in nature, is to strategically interact with rocks, debris, and other obstacles
to facilitate movement. To provide legged robots with this ability, we present
a novel approach that leverages avalanche dynamics to indirectly manipulate
objects on a granular slope. We use a Vision Transformer (ViT) to process image
representations of granular dynamics and robot excavation actions. The ViT
predicts object movement, which we use to determine which leg excavation action
to execute. We collect training data from 100 real physical trials and, at test
time, deploy our trained model in novel settings. Experimental results suggest
that our model can accurately predict object movements and achieve a success
rate $\geq 80\%$ in a variety of manipulation tasks with up to four obstacles,
and can also generalize to objects with different physics properties. To our
knowledge, this is the first paper to leverage granular media avalanche
dynamics to indirectly manipulate objects on granular slopes. Supplementary
material is available at https://sites.google.com/view/grain-corl2024/home.
