Influence-Aware Safety for Human-Robot Interaction

Abstract

In recent years, we have seen through recommender systems on social media how influential (and potentially harmful) algorithms can be in our lives, sometimes creating polarization and conspiracies that lead to unsafe behavior. Now that robots are also growing more common in the real world, we must be very careful to ensure that they are aware of the influence they will have on people, especially when it comes to safety---we do not want robots to cause any physical harm. In this thesis, we focus on the problem of influence-aware safe control for human-robot interaction in hopes of enabling robots to intentionally and positively influence people to make their interactions with robots safer and more efficient. We first study this problem from the safe control perspective by introducing a novel method for dealing with the multimodality of the robot's uncertainty over a human's intention inside a robust safe controller. Next, we explore different methods for generating influence-aware robot behavior from different levels of abstraction: action-directed, goal-directed, and strategy-directed. We ultimately find useful tools for designing robot behaviors that can proactively influence human collaborators towards positive outcomes. Finally, we join these thrusts by posing and solving the full influence-aware safety problem for human-robot interactions. We first show that explicitly enabling a robot to reason about the influence it has on a human collaborator can lead to more efficient interactions without sacrificing safety. Finally, we extend this paradigm to more general human-AI interactions, particularly people interacting with large language models (LLMs). We show that treating safety as failures in the physical environment in this domain allows us to automatically learn safety-preserving language actions and influential behavior that can steer interactions towards safe long-term outcomes from budget constraints in e-commerce to collision avoidance in driving. The work done in this thesis establishes a basis for several key research directions in the future. Specifically, researchers in AI safety can build on the influence-aware safety problem as a basis for generating safe AI systems that are explicitly aware of how their actions may change the behavior of human users in the real world. This work also enables future work to focus on safe long-term interactions between humans and robots. While this thesis focuses on influence during single interactions, the influence-aware safety problem can easily be applied to repeated interactions with human users, which is an important directions as AI chatbots, recommendation systems and physical robots continue becoming larger parts of our daily lives. Ultimately, this thesis will help researchers understand the importance of explicitly modeling the influence that autonomous agents have on people and how to use this influence to keep people safe.