论文标题
用户特定的适应性安全控制器有助于用户在人机协作中采用用户
User-specific, Adaptable Safety Controllers Facilitate User Adoption in Human-Robot Collaboration
论文作者
论文摘要
随着辅助和协作机器人在现实世界中变得更加普遍，我们需要开发安全的接口和控制器，这些界面和控制器可以安全地建立信任并鼓励采用。在这张蓝天纸中，我们讨论了可以适应人们安全偏好的共同发展任务和特定用户安全控制器的需求。我们认为，尽管大多数自适应控制器都专注于行为适应，但安全适应也是建立协作系统信任的主要考虑因素。此外，我们强调需要随着时间的推移进行适应，以说明用户在经验和信任构建中的偏好变化。我们为这些界面的外观以及使它们可行和成功所需的功能提供了一般配方。在此公式中，用户提供了演示和标记的安全价值，从中可以从中学习安全价值函数。可以通过更新演示的安全标签来更新这些值函数，以学习更新的功能。我们讨论如何在高级方面实施，以及一些有希望的方法和技术来实现这一目标。
As assistive and collaborative robots become more ubiquitous in the real-world, we need to develop interfaces and controllers that are safe for users to build trust and encourage adoption. In this Blue Sky paper, we discuss the need for co-evolving task and user-specific safety controllers that can accommodate people's safety preferences. We argue that while most adaptive controllers focus on behavioral adaptation, safety adaptation is also a major consideration for building trust in collaborative systems. Furthermore, we highlight the need for adaptation over time, to account for user's changes in preferences as experience and trust builds. We provide a general formulation for what these interfaces should look like and what features are necessary for making them feasible and successful. In this formulation, users provide demonstrations and labelled safety ratings from which a safety value function is learned. These value functions can be updated by updating the safety labels on demonstrations to learn an updated function. We discuss how this can be implemented at a high-level, as well as some promising approaches and techniques for enabling this.
