  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.
