3 papers indexed
Himanshu Gupta, Kelvin Aladum, Nisar Ahmed, Bradley Hayes, Zachary Sunberg
Target interception in crowded environments requires reaching a moving objective while navigating among multiple uncertain human agents. Since human navigation intent is not directly observable, the robot must reason over multiple possible future interaction outcomes. We formulat…
Yi-Shiuan Tung, Himanshu Gupta, Gyanig Kumar, Heyang Huang, Bradley Hayes, Alessandro Roncone
Shared autonomy enables humans and robots to collaboratively perform tasks by combining human input with autonomous assistance. Most prior work focuses on improving intent inference under a fixed environment, overlooking how workspace design itself affects inference difficulty. W…
Yi-Shiuan Tung, Yuni Wu, Wei Jiang, Alessandro Roncone, Bradley Hayes
Learning reward functions from human preferences is a widely used approach for aligning robot behavior with user expectations in human-robot interaction. Most existing approaches assume that humans evaluate uncertain outcomes using expected utility (EU), aggregating outcome utili…