Developing a Simple Model for Sand-Tool Interaction and Autonomously Shaping Sand

TL;DR

This paper presents a simplified model for autonomous sand shaping using a heightmap surface approach, formulating the task as a graph search problem solved with A-star and exploring reinforcement learning methods.

Contribution

It introduces a simplified surface-based model for sand-tool interaction and formulates the shape-shaping task as a graph search problem, enabling autonomous sand shaping.

Findings

A-star algorithm effectively plans sand shaping paths.

Preliminary reinforcement learning results show promise.

Simplified surface model reduces problem complexity.

Abstract

Autonomy for robots interacting with sand will enable a wide range of beneficial behaviors, from earth moving for construction and farming vehicles to navigating rough terrain for Mars rovers. The goal of this work is to shape sand into desired forms. Unlike other common autonomous tasks of achieving desired state of a robot, achieving a desired shape of a continuously deformable environment like sand is a much more challenging task. The state of robot can be described with a couple of states-x, y, z, roll, pitch, yaw-but the desired shape of sand can not be described with just a few values. Sand is an aggregation of billions of small particles. After simplifying the model of sand and tool interaction by looking only at the surface of the heightmap, we can formulate the problems into something that is still high dimensional (hundreds to thousands of state dimensions) but much more solvable. We show how this problem can be formulated into a graph search problem and solve it with the A-star algorithm and report preliminary results on using deep reinforcement learning methods like Deep Q-Network and Deep Deterministic Policy Gradient.