Abstract
Learning-based control approaches have shown great promise in performing complex tasks directly from high-dimensional perception data for real robotic systems. Nonetheless, the learned controllers can behave unexpectedly if the trajectories of the system divert from the training data distribution, which can compromise safety. In this work, we propose a control filter that wraps any reference policy and effectively encourages the system to stay in-distribution with respect to offline-collected safe demonstrations. Our methodology is inspired by Control Barrier Functions (CBFs), which are model-based tools from the nonlinear control literature that can be used to construct minimally invasive safe policy filters. While existing methods based on CBFs require a known low-dimensional state representation, our proposed approach is directly applicable to systems that rely solely on high-dimensional visual observations by learning in a latent state-space. We demonstrate that our method is effective for two different visuomotor control tasks in simulation environments, including both top-down and egocentric view settings.
Abstract (translated)
基于学习的控制方法在直接从高维度感知数据执行复杂任务方面表现出了巨大的潜力,对于真实的机器人系统而言也是如此。然而,如果系统的轨迹偏离了训练数据分布, learned控制器可能会表现出出乎意料的行为,这可能会影响安全性。在这项工作中,我们提出了一个控制滤波器,可以覆盖任何参考政策,并有效地鼓励系统在离线收集的安全演示中保持分布不变。我们的方法和灵感来自于控制屏障函数(CBFs),它们是非线性控制文献中基于模型的工具,可用于构建最小入侵的安全政策过滤器。虽然现有的基于CBFs的方法需要已知的低维度状态表示,但我们提出的方法可以直接适用于仅依靠隐含状态空间学习高维度视觉观察的系统。我们证明,我们的方法对于在模拟环境中执行两种不同的视觉 motor 控制任务非常有效,包括自上而下和自我意识视角设置。
URL
https://arxiv.org/abs/2301.12012
