Generating safe behaviors for autonomous systems is important as they continue to be deployed in the real world, especially around people. In this work, we focus on developing a novel safe controller for systems where there are multiple sources of uncertainty. We formulate a novel multimodal safe control method, called the Multimodal Safe Set Algorithm (MMSSA) for the case where the agent has uncertainty over which discrete mode the system is in, and each mode itself contains additional uncertainty. To our knowledge, this is the first energy-function-based safe control method applied to systems with multimodal uncertainty. We apply our controller to a simulated human-robot interaction where the robot is uncertain of the human's true intention and each potential intention has its own additional uncertainty associated with it, since the human is not a perfectly rational actor. We compare our proposed safe controller to existing safe control methods and find that it does not impede the system performance (i.e. efficiency) while also improving the safety of the system.

翻译：随着自主系统持续部署到现实环境中，尤其是在人类周围时，为其生成安全行为至关重要。本研究聚焦于为存在多种不确定性来源的系统开发一种新型安全控制器。我们针对智能体对系统所处的离散模态具有不确定性、且各模态本身包含额外不确定性的场景，提出了一种新颖的多模态安全控制方法——多模态安全集算法（MMSSA）。据我们所知，这是首个应用于多模态不确定性系统的基于能量函数的安全控制方法。我们将所提出的控制器应用于模拟的人机交互场景，其中机器人对人类真实意图存在不确定性，且由于人类并非完全理性的行为者，每种潜在意图均关联着其独有的额外不确定性。通过将所提出的安全控制器与现有安全控制方法进行对比，我们发现该方法在提升系统安全性的同时，并未降低系统性能（即效率）。