用数据学习机器人非线性动力学，实现高效安全控制。
Whole-Body Safe Control of Robotic Systems with Koopman Neural Dynamics
- 基于数据学习的Koopman嵌入，将非线性系统转为线性模型
- 在单个二次规划中同时实现轨迹跟踪与避障，精度高
- 适合需实时安全控制的机械臂与四足机器人
控制具有强非线性、高维度动态特性的机器人仍具挑战性，因直接带安全约束的非线性优化在实时场景中通常不可行。Koopman算子提供了一种在升维空间中线性表示非线性系统的方法，使高效线性控制成为可能。本文提出一种数据驱动框架，从数据中学习Koopman嵌入与算子，并将其与安全集算法（SSA）集成。该方法可在单一二次规划（QP）中求解轨迹跟踪与安全约束，无需额外安全过滤器，确保可行性与最优性。我们在Kinova Gen3机械臂和Go2四足机器人上验证了该方法，结果表明其具备精确轨迹跟踪能力与有效障碍物避让性能。
原文摘要 · Abstract (English)
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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