Whole-Body Safe Control of Robotic Systems with Koopman Neural Dynamics

• URL: http://arxiv.org/abs/2603.03740v3
• Date: Thu, 19 Mar 2026 04:11:25 GMT
• Title: Whole-Body Safe Control of Robotic Systems with Koopman Neural Dynamics
• Abstract summary: 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)<n>We validate the method on a Kinova Gen3 manipulator and a Go2 quadruped, showing accurate tracking and obstacle avoidance.
• Score: 20.955892282082793
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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