用解析模型加速液压四足机器人仿真，实现真实机器人的稳定控制。
Learning Quadrupedal Locomotion for a Heavy Hydraulic Robot Using an Actuator Model
- 构建基于流体动力学的解析执行器模型，实时预测12个关节扭矩。
- 在数据有限场景下优于神经网络模型，训练后直接部署于300公斤级机器人。
- 首次实现重型液压四足机器人强化学习的稳定仿真到现实迁移。
大型液压机器人的仿真到现实（sim-to-real）迁移面临巨大挑战，源于固有的慢控制响应和复杂的流体动力学。多连通缸结构及各缸流速差异导致整体动力学复杂，难以对所有关节进行精细建模，不适用于强化学习（RL）应用。本文提出一种由液压动力学驱动的解析执行器模型，可对全部12个执行器的关节扭矩进行预测，耗时不足1微秒，支持强化学习环境中的快速计算。与基于神经网络的执行器模型相比，本方法在数据受限场景下表现更优。基于该模型训练的运动策略成功部署于一台超过300公斤的液压四足机器人上，首次实现了重型液压四足机器人上稳定可靠的指令跟踪行走，验证了先进的仿真到现实迁移能力。
原文摘要 · Abstract (English)
The simulation-to-reality (sim-to-real) transfer of large-scale hydraulic robots presents a significant challenge in robotics because of the inherent slow control response and complex fluid dynamics. The complex dynamics result from the multiple interconnected cylinder structure and the difference in fluid rates of the cylinders. These characteristics complicate detailed simulation for all joints, making it unsuitable for reinforcement learning (RL) applications. In this work, we propose an analytical actuator model driven by hydraulic dynamics to represent the complicated actuators. The model predicts joint torques for all 12 actuators in under 1 microsecond, allowing rapid processing in RL environments. We compare our model with neural network-based actuator models and demonstrate the advantages of our model in data-limited scenarios. The locomotion policy trained in RL with our model is deployed on a hydraulic quadruped robot, which is over 300 kg. This work is the first demonstration of a successful transfer of stable and robust command-tracking locomotion with RL on a heavy hydraulic quadruped robot, demonstrating advanced sim-to-real transferability.
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