Learning Granular Media Avalanche Behavior for Indirectly Manipulating Obstacles on a Granular Slope

Haodi Hu, Feifei Qian, Daniel Seita

Keywords: Granular media, Avalanche dynamics, Legged robots.

TL;DR: In this work, we propose Granular Robotic Avalanche INteraction (GRAIN), a novel learning-based method for leveraging granular avalanche dynamics for indirectly manipulating objects on a granular slope.

Abstract: 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 ≥ 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.

Website: https://sites.google.com/view/grain-corl2024/home

Student Paper: yes

Submission Number: 153