Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning

TL;DR

This paper demonstrates the first end-to-end deep reinforcement learning approach for multi-agent robot soccer using egocentric vision, successfully transferring complex learned behaviors from simulation to real robots.

Contribution

It introduces a novel simulation environment with realistic rendering and multi-agent RL techniques for training robot soccer policies directly from raw pixel data.

Findings

Agents achieve performance comparable to ground-truth state policies

Emergent active perception behaviors like object tracking and ball seeking

Successful transfer of simulation-trained policies to real robots

Abstract

We apply multi-agent deep reinforcement learning (RL) to train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision. This setting reflects many challenges of real-world robotics, including active perception, agile full-body control, and long-horizon planning in a dynamic, partially-observable, multi-agent domain. We rely on large-scale, simulation-based data generation to obtain complex behaviors from egocentric vision which can be successfully transferred to physical robots using low-cost sensors. To achieve adequate visual realism, our simulation combines rigid-body physics with learned, realistic rendering via multiple Neural Radiance Fields (NeRFs). We combine teacher-based multi-agent RL and cross-experiment data reuse to enable the discovery of sophisticated soccer strategies. We analyze active-perception behaviors including object tracking and ball seeking that emerge when simply optimizing perception-agnostic soccer play. The agents display equivalent levels of performance and agility as policies with access to privileged, ground-truth state. To our knowledge, this paper constitutes a first demonstration of end-to-end training for multi-agent robot soccer, mapping raw pixel observations to joint-level actions, that can be deployed in the real world. Videos of the game-play and analyses can be seen on our website https://sites.google.com/view/vision-soccer .