End-to-End Deep Imitation Learning: Robot Soccer Case Study

TL;DR

This paper presents an end-to-end deep imitation learning approach using convolutional neural networks to teach a robot soccer player to dribble and search for the ball, demonstrating promising results in a realistic simulator.

Contribution

It introduces a deep CNN-based policy learning method for robot soccer, utilizing raw camera images as input for end-to-end imitation learning.

Findings

Robot learns to search and dribble the ball

Achieves 4 goals in 20 test episodes

Struggles with goal alignment

Abstract

In imitation learning, behavior learning is generally done using the features extracted from the demonstration data. Recent deep learning algorithms enable the development of machine learning methods that can get high dimensional data as an input. In this work, we use imitation learning to teach the robot to dribble the ball to the goal. We use B-Human robot software to collect demonstration data and a deep convolutional network to represent the policies. We use top and bottom camera images of the robot as input and speed commands as outputs. The CNN policy learns the mapping between the series of images and speed commands. In 3D realistic robotics simulator experiments, we show that the robot is able to learn to search the ball and dribble the ball, but it struggles to align to the goal. The best-proposed policy model learns to score 4 goals out of 20 test episodes.