CRII: RI: Active Learning of Preferences for Human-Aware Autonomy

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Humans' preferences play a key role in specifying how robotics systems should act, i.e., how an assistive robot arm should move, or how an autonomous car should drive. The learned human preferences are an important element in planning for interactive autonomous systems, e.g., robots collaborating with different types of human teammates, or shared autonomy with a human to efficiently teleoperate a robot arm. One hopes that learning techniques can be used to learn reward functions representing humans' preferences for robotics applications. However, a significant part of the success of learning algorithms can be attributed to the availability of large amounts of labeled data. Unfortunately, collecting and labeling data can be costly and time-consuming in robotics applications. In addition, humans are not always capable of reliably assigning a success value (reward) to a given robot action, and their demonstrations are usually suboptimal due to the difficulty of operating robots with more than a few degrees of freedom. The proposed research develops foundational techniques to address the key challenges of using learning techniques in human-robot interaction. The goal of this project is to develop efficient methods and algorithms to first better model and understand humans preferences while operating, interacting, and collaborating with robots. Furthermore, the investigator will design algorithms that plan for robots that are aware of such preferences and can initiate a safe and seamless interaction with humans. This project involves two main contributions: (1) Developing efficient and active algorithms to learn probabilistic mixture models for humans preferences about how a robot should operate based on comparisons and rankings., and (2) Developing planning algorithms for robots that leverage humans preferences to enable seamless shared autonomy and more efficient human-robot interaction. Preliminary results in the domain of autonomous driving suggest that one can learn driving preferences of humans and this approach can improve efficiency and safety of robotics systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.