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.

翻译：在沙质斜坡上实现腿式机器人运动具有挑战性，这源于颗粒介质的复杂动力学特性以及缺乏坚实表面可能阻碍运动。受鬼蟹等自然界生物的启发，一种有前景的策略是通过策略性地与岩石、碎屑及其他障碍物互动来辅助移动。为使腿式机器人具备此能力，我们提出一种新颖方法，利用雪崩动力学间接操控颗粒斜坡上的物体。我们采用视觉Transformer（ViT）处理颗粒动力学及机器人挖掘动作的图像表征。ViT预测物体运动，我们据此决定执行何种腿部挖掘动作。我们从100次真实物理试验中收集训练数据，并在测试时将训练好的模型部署于新场景中。实验结果表明，我们的模型能准确预测物体运动，在涉及最多四个障碍物的多种操控任务中成功率≥80%，并能泛化至具有不同物理属性的物体。据我们所知，这是首篇利用颗粒介质雪崩动力学间接操控颗粒斜坡上物体的论文。补充材料详见 https://sites.google.com/view/grain-corl2024/home。