# The Open Ant: A Robot Platform for Reinforcement Learning Research

arXiv: https://arxiv.org/abs/2607.18488 · HTML: https://mlsanity.com/paper/2607.18488/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 20 of 20)

- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [DayDreamer: World Models for Physical Robot Learning](https://mlsanity.com/paper/2206.14176/) (2022)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Residual Policy Learning](https://mlsanity.com/paper/1812.06298/) (2018)
- [Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds](https://mlsanity.com/paper/2205.06908/) (2022)
- [A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning](https://mlsanity.com/paper/2208.07860/) (2022)
- [Benchmarking Reinforcement Learning Algorithms on Real-World Robots](https://mlsanity.com/paper/1809.07731/) (2018)
- [Setting up a Reinforcement Learning Task with a Real-World Robot](https://mlsanity.com/paper/1803.07067/) (2018)
- [Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots](https://mlsanity.com/paper/2203.12759/) (2022)
- [Maximum diffusion reinforcement learning](https://mlsanity.com/paper/2309.15293/) (2023)
- [MAGIC-VFM: Meta-learning Adaptation for Ground Interaction Control with Visual Foundation Models](https://mlsanity.com/paper/2407.12304/) (2024)
- [Adaptive Nonlinear Control of Fixed-Wing VTOL with Airflow Vector Sensing](https://mlsanity.com/paper/2003.07558/) (2020)
- [An Idiosyncrasy of Time-discretization in Reinforcement Learning](https://mlsanity.com/paper/2406.14951/) (2024)
- [Improving cosmological reach of a gravitational wave observatory using Deep Loop Shaping](https://mlsanity.com/paper/2509.14016/) (2025)
- [Fast Non-Episodic Adaptive Tuning of Robot Controllers with Online Policy Optimization](https://mlsanity.com/paper/2507.10914/) (2025)
- [Learning Without Time-Based Embodiment Resets in Soft-Actor Critic](https://mlsanity.com/paper/2512.06252/) (2025)

## Abstract (arXiv)

Reinforcement learning (RL) research has demonstrated success in both physical and simulated domains; however, the predominant methodology remains rooted in simulations. The predominance of simulations makes translating research to physical reality uncertain for both algorithms and researchers. We propose a physical platform that is designed to simplify the transition. In this paper, we present the Open Ant: a physical variant of the commonly used Gymnasium Ant environment, along with a simulation. We demonstrate that competent walking policies can be learned from scratch in approximately one hour directly from the physical robot's experience for two substantially different RL algorithms: SARSA(λ) and Soft Actor-Critic (SAC). Separately, we show policies that were learned in simulation transfer to reality. We also examine how well the platform supports a nimble experimental ecosystem. Specifically, we observe the speed with which new users from diverse backgrounds achieve their first success with the platform, and how easily the platform can be repaired and updated when hardware issues arise. Both the hardware design and software are available as open-source on GitHub for ease of customization. In summary, we advocate for the use of the Open Ant for RL researchers who frequently use simulated environments, so they can more easily include robot experiments in their evaluations.