用随机搜索统一理解机器人优化算法
An Introduction to Zero-Order Optimization Techniques for Robotics
- 以随机搜索为视角，统一分析多种机器人优化方法
- 在无梯度条件下实现轨迹与策略优化，有效避开局部极小
- 适合机器人控制、强化学习初学者快速掌握核心思想
零阶优化技术因能处理不可微函数并逃离局部极小值，在机器人领域日益受到关注。本文提出一种基于随机搜索的数学教程，提供了一个简洁且统一的视角，用于理解机器人中广泛使用的各类算法。借助这一视角，我们将多种轨迹优化方法归入同一框架，并推导出新型高效的强化学习算法。
原文摘要 · Abstract (English)
Zero-order optimization techniques are becoming increasingly popular in robotics due to their ability to handle non-differentiable functions and escape local minima. These advantages make them particularly useful for trajectory optimization and policy optimization. In this work, we propose a mathematical tutorial on random search. It offers a simple and unifying perspective for understanding a wide range of algorithms commonly used in robotics. Leveraging this viewpoint, we classify many trajectory optimization methods under a common framework and derive novel competitive RL algorithms.
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