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 more 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, in ongoing and proposed work, we join these two thrusts by introducing an influence-aware predictive model of a human collaborator inside the safe control loop to ensure long-term safety while potentially being less conservative than existing robust safe control methods. The proposed work builds on high-dimensional, modern, learning-based techniques so we can apply the influence-aware safety idea to real robotic systems. Ultimately, we hope that the work done in this thesis will help researchers in robotics (and beyond) to understand the importance of explicitly modeling the influence that autonomous agents have on people and how to use this understanding to keep people safe.

Thesis Committee Members:
Changliu Liu, Co-chair
Andrea Bajcsy, Co-chair
Aditi Raghunathan
Guy Rosman, Toyota Research Institute

More Information