CBO方法让机器人轨迹优化突破局部最优，可全局收敛。
Consensus-based optimization (CBO): Towards Global Optimality in Robotics
- 基于共识机制的零阶优化，不依赖梯度估计
- 三种复杂场景下均优于MPPI、CEM等现有方法
- 适合追求全局最优的高维、长时程机器人规划
零阶优化在机器人轨迹与策略设计中受到广泛关注。然而，现有方法（如MPPI、CEM、CMA-ES）多为局部优化，依赖梯度估计。本文将共识基优化（CBO）引入机器人领域，在弱假设下保证收敛至全局最优。通过理论分析与实例演示，阐明CBO与传统方法的本质差异。为验证其可扩展性，我们测试了三个挑战性轨迹优化任务：(1) 简单系统的长时程问题，(2) 高度欠驱动系统的动态平衡问题，(3) 仅含终端代价的高维问题。结果表明，CBO在所有三类设置中均取得更低代价，显著优于现有方法，为机器人全局轨迹优化提供了新框架。
原文摘要 · Abstract (English)
Zero-order optimization has recently received significant attention for designing optimal trajectories and policies for robotic systems. However, most existing methods (e.g., MPPI, CEM, and CMA-ES) are local in nature, as they rely on gradient estimation. In this paper, we introduce consensus-based optimization (CBO) to robotics, which is guaranteed to converge to a global optimum under mild assumptions. We provide theoretical analysis and illustrative examples that give intuition into the fundamental differences between CBO and existing methods. To demonstrate the scalability of CBO for robotics problems, we consider three challenging trajectory optimization scenarios: (1) a long-horizon problem for a simple system, (2) a dynamic balance problem for a highly underactuated system, and (3) a high-dimensional problem with only a terminal cost. Our results show that CBO is able to achieve lower costs with respect to existing methods on all three challenging settings. This opens a new framework to study global trajectory optimization in robotics.
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