Consensus-Based Optimization (CBO) for Trajectory Optimization in Robotics

MCML Authors

Massimo Fornasier

Prof. Dr.

Core PI

Applied Numerical Analysis

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. This is a shorter version of our paper at https://arxiv.org/pdf/2602.06868.

inproceedings SJF+26a

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FOR @ICRA 2026

2nd Workshop on Frontiers of Optimization for Robotics at the IEEE International Conference on Robotics and Automation. Vienna, Austria, Jun 01-05, 2026.

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Authors

X. Sun • A. Jordana • M. Fornasier • J. Etesami • M. Khadiv

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Links

URL

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Research Area

 A2 | Mathematical Foundations