Whole-Body Safe Control of Robotic Systems with Koopman Neural Dynamics
Sebin Jung, Abulikemu Abuduweili, Jiaxing Li, Changliu Liu
AI summary
Problem
Real-time safe control of high-dimensional, nonlinear robots is hindered by computationally prohibitive nonlinear optimization and frequent constraint infeasibility at safety boundaries.
Approach
The method learns a Koopman operator to globally linearize robot dynamics in a lifted space, then integrates a redesigned safety specification directly into a single MPC quadratic program, using adversarial fine-tuning to guarantee constraint feasibility.
Key results
- Unified Koopman-MPC formulation solves tracking and safety in a single QP
- Adversarial fine-tuning of the safety index drastically reduces QP infeasibility at safe set boundaries
- Successful sim-to-real deployment on a Kinova Gen3 manipulator with minimal retraining
- Effective obstacle avoidance and trajectory tracking demonstrated on fixed-base and floating-base robots
Why it matters
Provides a scalable, real-time safe control solution for high-DOF robots that eliminates the need for separate safety filters or exact dynamic models, accelerating practical deployment in cluttered environments.
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 nonlin- ear 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.