RL 的仿真到真实迁移

在仿真中训练 RL 策略并迁移到真实机器人：域随机化、课程学习、奖励塑形和安全约束。

Learning Objectives

1. The Sim-to-Real Pipeline

2. Domain Randomization for RL

2.1 Visual Randomization

2.2 Dynamics Randomization

2.3 Implementation in Isaac Lab

3. Curriculum Learning

3.1 Task Difficulty Scheduling

3.2 Automatic Curriculum

4. Reward Shaping

4.1 Potential-Based Shaping

4.2 Sparse vs. Dense Rewards

4.3 Reward Engineering Tips

5. Safety Constraints

5.1 Constrained MDP

5.2 Safe Exploration

6. Success Stories

6.1 OpenAI Rubik's Cube

6.2 DeepLoco

6.3 Robotic Assembly

7. Practical Checklist

Exercises

References