用数据驱动方法自动估算稳定性参数，让机器人无需模型也能安全控速。
Towards Data-Driven Model-Free Safety-Critical Control
- 用神经网络从数据学出李雅普诺夫函数，估计控制器的衰减速率
- 通过概率安全条件，容忍稳定性偏差，提升对不确定性的鲁棒性
- 在UR5e机器人上验证，无需系统模型即可实现安全速度控制
本文提出一种数据驱动的无模型安全控制框架，用于通用机器人系统的安全速度控制。传统无模型控制屏障函数（CBFs）依赖于指数稳定速度控制器的衰减速率和设计参数（如CBFs中的α），但实际中衰减速率常不可知，导致α需手动调参。为此，本文采用神经网络从数据中学习李雅普诺夫函数，并据此估计系统内置速度控制器的最大衰减速率。进一步地，为将估计的衰减速率与无模型CBFs结合，推导出基于切尔诺夫界的概率安全条件，引入对稳定性违反率的置信边界，增强对稳定性偏差的鲁棒性。该框架已在UR5e机器人上多个实验场景中测试，验证了其在无模型条件下实现安全速度控制的有效性。
原文摘要 · Abstract (English)
This paper presents a framework for enabling safe velocity control of general robotic systems using data-driven model-free Control Barrier Functions (CBFs). Model-free CBFs rely on an exponentially stable velocity controller and a design parameter (e.g. alpha in CBFs); this design parameter depends on the exponential decay rate of the controller. However, in practice, the decay rate is often unavailable, making it non-trivial to use model-free CBFs, as it requires manual tuning for alpha. To address this, a Neural Network is used to learn the Lyapunov function from data, and the maximum decay rate of the systems built-in velocity controller is subsequently estimated. Furthermore, to integrate the estimated decay rate with model-free CBFs, we derive a probabilistic safety condition that incorporates a confidence bound on the violation rate of the exponential stability condition, using Chernoff bound. This enhances robustness against uncertainties in stability violations. The proposed framework has been tested on a UR5e robot in multiple experimental settings, and its effectiveness in ensuring safe velocity control with model-free CBFs has been demonstrated.
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