Whole-Body Safe Control of Robotic Systems with Koopman Neural Dynamics
Abstract
Controlling robots with strongly nonlinear, high-dimensional dynamics remains challenging, as direct nonlinear optimization with safety constraints is often intractable in real time. The Koopman operator offers a way to represent nonlinear systems linearly in a lifted space, enabling the use of efficient linear control. We propose a data-driven framework that learns a Koopman embedding and operator from data, and integrates the resulting linear model with the Safe Set Algorithm (SSA). This allows the tracking and safety constraints to be solved in a single quadratic program (QP), ensuring feasibility and optimality without a separate safety filter. We validate the method on a Kinova Gen3 manipulator and a Go2 quadruped, showing accurate tracking and obstacle avoidance.
Keywords
Cite
@article{arxiv.2603.03740,
title = {Whole-Body Safe Control of Robotic Systems with Koopman Neural Dynamics},
author = {Sebin Jung and Abulikemu Abuduweili and Jiaxing Li and Changliu Liu},
journal= {arXiv preprint arXiv:2603.03740},
year = {2026}
}