An Open-source Sim2Real Approach for Sensor-independent Robot Navigation in a Grid

TL;DR

This paper introduces an open-source Sim2Real method that transfers reinforcement learning policies from simulation to a real quadruped robot for grid navigation, eliminating the need for expensive sensors.

Contribution

It presents a novel pipeline for transferring RL-based motion policies from a simulated grid environment to a physical quadruped robot, enabling sensor-independent navigation.

Findings

Successful transfer of RL policies from simulation to real robot

Autonomous obstacle avoidance in grid environments

Open-source implementation available on GitHub

Abstract

This paper presents a Sim2Real (Simulation to Reality) approach to bridge the gap between a trained agent in a simulated environment and its real-world implementation in navigating a robot in a similar setting. Specifically, we focus on navigating a quadruped robot in a real-world grid-like environment inspired by the Gymnasium Frozen Lake -- a highly user-friendly and free Application Programming Interface (API) to develop and test Reinforcement Learning (RL) algorithms. We detail the development of a pipeline to transfer motion policies learned in the Frozen Lake simulation to a physical quadruped robot, thus enabling autonomous navigation and obstacle avoidance in a grid without relying on expensive localization and mapping sensors. The work involves training an RL agent in the Frozen Lake environment and utilizing the resulting Q-table to control a 12 Degrees-of-Freedom (DOF) quadruped robot. In addition to detailing the RL implementation, inverse kinematics-based quadruped gaits, and the transfer policy pipeline, we open-source the project on GitHub and include a demonstration video of our Sim2Real transfer approach. This work provides an accessible, straightforward, and low-cost framework for researchers, students, and hobbyists to explore and implement RL-based robot navigation in real-world grid environments.