用新仿真工具训练四足机器人，实现零样本直接部署到真实机器狗。
Isaac Sim-to-Real: Reinforcement Learning based Locomotion for Quadrupeds
- 基于Isaac Sim与Isaac Lab的强化学习框架，实现全身控制。
- 真实机器狗上达到2.0米/秒线速度、1.8弧度/秒角速度，抗干扰更强。
- 无需微调即可从仿真直接运行，适合快速部署于真实四足平台。
近年来，基于学习的运动控制方法受到广泛关注，展现出复杂腿式运动与全身控制能力。强化学习（RL）作为主要方法，通常依赖高性能仿真工具，提供高效可控的训练环境。然而，仿真中表现良好的策略在物理系统部署时常因“仿真到现实差距”遭遇意外挑战。本文提出一种鲁棒的强化学习运动控制框架，利用Nvidia新推出的仿真工具Isaac Sim及其配套的RL框架Isaac Lab进行训练，实现了零样本仿真到现实的策略迁移。通过在真实硬件Unitree Go1上的实验验证，该策略的轨迹跟踪性能与原生控制器相当，且对大扰动恢复能力更强，可实现最高2.0米/秒的线速度和1.8弧度/秒的角速度。
原文摘要 · Abstract (English)
Learning-based approaches to locomotion have risen in popularity in recent years, showing the capability for complex legged locomotion and whole-body control. Reinforcement learning (RL), the primary learning-based approach for locomotion, often utilizes a high-performance simulation tool, providing a controlled and efficient training and development environment. However, policies that perform well in simulation frequently encounter unexpected challenges when deployed on a physical system, known as the sim-to-real gap. This work presents a robust RL locomotion framework capable of whole-body control. The proposed RL framework utilizes Nvidia's new set of simulation tools, Isaac Sim, and its companion RL framework, Isaac Lab, for training, achieving a zero-shot sim-to-real policy. The performance of our policy is validated on physical hardware using the Unitree Go1, with experimental results showing similar velocity tracking performance to the quadruped's integrated controller, with a greater ability to recover from large disturbances, and achieve linear velocities of 2.0 m/s and angular velocities of 1.8 rad/s.
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