外观
论文arXiv 2607.184883 个月前
Open Ant：用于强化学习研究的机器人平台
原文链接：The Open Ant: A Robot Platform for Reinforcement Learning Research（arXiv 2607.18488 · 发布日期：2026-07-20）
The Open Ant: A Robot Platform for Reinforcement Learning Research
为降低强化学习从仿真走向实物的不确定性，Open Ant提供实机与仿真平台，并在行走学习和仿真到现实迁移中验证。
核心要点
- 强化学习研究主要依赖仿真，算法和研究者向物理现实迁移仍存在不确定性。
- Open Ant是常用Gymnasium Ant环境的物理变体，并配套提供仿真。
- SARSA(λ)与SAC可在实机经验上约一小时从零学会行走，仿真策略也可迁移到现实。
- 论文还观察新用户首次成功速度及硬件故障后的维修、更新便利性；软硬件已在GitHub开源。
验证范围与局限：验证范围为Open Ant平台上的实机行走学习、仿真到现实迁移，以及用户上手和硬件维护观察。
英文摘要 (Abstract)
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.
作者：Elena Sorina Lupu, Patrick Spieler, Khurram Javed, Kristopher De Asis, John D. Martin, Martha Steenstrup, Joseph Modayil
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