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

2026-02-06

AI Summary

This work proposes the first application of consensus-based optimization (CBO)—a gradient-free framework with provable global convergence guarantees—to robotic trajectory and policy optimization. Addressing the limitations of existing zeroth-order methods such as MPPI, CEM, and CMA-ES, which often stagnate in local optima, the proposed approach leverages swarm intelligence to effectively handle high-dimensional, long-horizon, and underactuated robotic systems. Evaluated across three challenging scenarios, the method consistently outperforms state-of-the-art algorithms, achieving significantly lower optimization costs in terms of terminal error, scalability to high-dimensional state spaces, and performance on extended-horizon tasks. These results demonstrate the efficacy and superiority of CBO for complex robotic optimization problems where global convergence and robustness are critical.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• global optimality
• trajectory optimization
• zero-order optimization
• robotics
• consensus-based optimization

Innovation

• Consensus-based Optimization
• Global Optimality
• Zero-order Optimization
• Trajectory Optimization
• Robotics