Robotics: Science and Systems XXII

Consensus-based optimization (CBO): Towards Global Optimality in Robotics

Xudong Sun, Armand Jordana, Massimo Fornasier, Jalal Etesami, Majid Khadiv

Abstract:

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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Bibtex:

  
@INPROCEEDINGS{SunX-RSS-26, 
    AUTHOR    = {Xudong Sun AND Armand Jordana AND Massimo Fornasier AND Jalal Etesami AND Majid Khadiv}, 
    TITLE     = {{Consensus-based optimization (CBO): Towards Global Optimality in Robotics}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.161} 
}