用仿真训练的AI让四足机器人无传感器导航网格环境。
An Open-source Sim2Real Approach for Sensor-independent Robot Navigation in a Grid
- 用强化学习在虚拟冰面环境中训练，生成控制策略
- 将训练好的策略迁移到真实四足机器人，实现自主避障
- 开源方案适合学生和爱好者低成本实践强化学习导航
本文提出一种Sim2Real（仿真到现实）方法，弥合仿真训练与真实应用之间的差距，实现四足机器人在类似Gymnasium Frozen Lake的网格环境中自主导航。研究聚焦于在该仿真环境下训练强化学习（RL）智能体，并利用生成的Q表控制一台12自由度的四足机器人，在无需昂贵定位与建图传感器的前提下完成网格内自主导航与障碍物规避。工作包括在Frozen Lake环境中训练RL代理、基于逆运动学设计四足步态，以及构建策略迁移管道。项目已开源至GitHub，并附演示视频。本研究为科研人员、学生及爱好者提供了一个可访问、简单且低成本的框架，用于在真实网格环境中探索和实现基于强化学习的机器人导航。
原文摘要 · Abstract (English)
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.
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