开源框架实现四足机械臂强化学习控制的仿真到现实迁移
A Framework for Deploying Learning-based Quadruped Loco-Manipulation
- 基于ROS构建统一管道，打通Sim-to-Sim与Sim-to-Real全流程
- 在Isaac Gym和MuJoCo间发现接触模型差异影响策略表现
- 实机试验证明全身协同控制提升抓取范围与操作精度
四足移动操作机器人具备敏捷运动与操作潜力，但其控制与从仿真到现实的迁移仍具挑战。强化学习（RL）有望实现全身控制，但多数框架为专有且难以在真实硬件上复现。本文提出一个开源管道，用于在Unitree B1四足机器人搭载Z1机械臂上训练、评测和部署基于RL的控制器。该框架通过ROS统一仿真到仿真（sim-to-sim）与仿真到现实（sim-to-real）迁移路径，重实现了Isaac Gym中的策略，并通过硬件抽象层扩展至MuJoCo，最终部署于物理硬件。仿真对比显示Isaac Gym与MuJoCo的接触模型存在差异，影响策略行为；真实世界遥控抓取实验表明，全身协同控制显著扩展了操作范围并优于浮基基线方法。该管道提供透明可复现的开发分析基础，将开源以支持后续研究。
原文摘要 · Abstract (English)
Quadruped mobile manipulators offer strong potential for agile loco-manipulation but remain difficult to control and transfer reliably from simulation to reality. Reinforcement learning (RL) shows promise for whole-body control, yet most frameworks are proprietary and hard to reproduce on real hardware. We present an open pipeline for training, benchmarking, and deploying RL-based controllers on the Unitree B1 quadruped with a Z1 arm. The framework unifies sim-to-sim and sim-to-real transfer through ROS, re-implementing a policy trained in Isaac Gym, extending it to MuJoCo via a hardware abstraction layer, and deploying the same controller on physical hardware. Sim-to-sim experiments expose discrepancies between Isaac Gym and MuJoCo contact models that influence policy behavior, while real-world teleoperated object-picking trials show that coordinated whole-body control extends reach and improves manipulation over floating-base baselines. The pipeline provides a transparent, reproducible foundation for developing and analyzing RL-based loco-manipulation controllers and will be released open source to support future research.
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