解决机器人行走强化学习仿真到现实的迁移难题
Sim-to-Real Transfer in Deep Reinforcement Learning for Bipedal Locomotion
- 从物理模型、接触力、状态估计等四方面分析仿真差距
- 提出提升仿真精度与增强策略鲁棒性的双路径方案
- 适合机器人控制与强化学习研究者参考
本文针对双足行走中深度强化学习的仿真到现实（sim-to-real）迁移问题，分析了其在各类控制架构中的挑战。通过剖析仿真差距的主要来源——机器人动力学、接触建模、状态估计和数值求解器，提出两种互补策略：一是通过模型中心方法系统提升仿真物理精度；二是通过仿真内鲁棒训练与部署后自适应，使策略具备对模型误差的内在抗性。最后，将二者整合为可操作的战略框架，为开发与评估稳健的sim-to-real解决方案提供清晰路径。
原文摘要 · Abstract (English)
This chapter addresses the critical challenge of simulation-to-reality (sim-to-real) transfer for deep reinforcement learning (DRL) in bipedal locomotion. After contextualizing the problem within various control architectures, we dissect the ``curse of simulation'' by analyzing the primary sources of sim-to-real gap: robot dynamics, contact modeling, state estimation, and numerical solvers. Building on this diagnosis, we structure the solutions around two complementary philosophies. The first is to shrink the gap through model-centric strategies that systematically improve the simulator's physical fidelity. The second is to harden the policy, a complementary approach that uses in-simulation robustness training and post-deployment adaptation to make the policy inherently resilient to model inaccuracies. The chapter concludes by synthesizing these philosophies into a strategic framework, providing a clear roadmap for developing and evaluating robust sim-to-real solutions.
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