# RealAnt: An Open-Source Low-Cost Quadruped for Education and Research in Real-World Reinforcement Learning

arXiv: https://arxiv.org/abs/2011.03085 · HTML: https://mlsanity.com/paper/2011.03085/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Robust Localization, Mapping, and Navigation for Quadruped Robots](https://mlsanity.com/paper/2505.02272/) (2025)
- [BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO](https://mlsanity.com/paper/2406.17490/) (2024)
- [Coordinating Spinal and Limb Dynamics for Enhanced Sprawling Robot Mobility](https://mlsanity.com/paper/2504.14103/) (2025)

## Abstract (arXiv)

Current robot platforms available for research are either very expensive or unable to handle the abuse of exploratory controls in reinforcement learning. We develop RealAnt, a minimal low-cost physical version of the popular `Ant' benchmark used in reinforcement learning. RealAnt costs only ~350 EUR (410) in materials and can be assembled in less than an hour. We validate the platform with reinforcement learning experiments and provide baseline results on a set of benchmark tasks. We demonstrate that the RealAnt robot can learn to walk from scratch from less than 10 minutes of experience. We also provide simulator versions of the robot (with the same dimensions, state-action spaces, and delayed noisy observations) in the MuJoCo and PyBullet simulators. We open-source hardware designs, supporting software, and baseline results for educational use and reproducible research.