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：为自主系统生成安全行为至关重要，因为它们在现实世界（尤其是人类周围环境）中的部署日益广泛。本研究致力于为存在多源不确定性的系统开发一种新型安全控制器。针对智能体对系统所处离散模态存在不确定性、且每个模态本身包含额外不确定性的情况，我们提出了一种创新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。我们将该控制器应用于模拟人机交互场景，其中机器人对人类真实意图存在不确定性，且每个潜在意图因人类并非完全理性行为体而具有其特有的附加不确定性。通过将所提出的安全控制器与现有安全控制方法进行比较，我们发现该方法在提升系统安全性的同时，不会影响系统性能（即运行效率）。