DeepAI AI Chat
Log In Sign Up

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

by   Wooshik Kim, et al.
Carnegie Mellon University

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.


page 1

page 5

page 6

page 9


Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning

For robots to coexist with humans in a social world like ours, it is cru...

Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot

We address the problem of enabling quadrupedal robots to perform precise...

Control and Coordination of a SWARM of Unmanned Surface Vehicles using Deep Reinforcement Learning in ROS

An unmanned surface vehicle (USV) can perform complex missions by contin...

Robot Navigation in a Crowd by Integrating Deep Reinforcement Learning and Online Planning

It is still an open and challenging problem for mobile robots navigating...

Few-Shot Goal Inference for Visuomotor Learning and Planning

Reinforcement learning and planning methods require an objective or rewa...

PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement Learning

Robots can now learn how to make decisions and control themselves, gener...