Generating Plans that Predict Themselves
Jaime F. Fisac, Chang Liu, Jessica B. Hamrick, S. Shankar Sastry, J., Karl Hedrick, Thomas L. Griffiths, and Anca D. Dragan

TL;DR
This paper introduces a method for generating robot plans that are easily predictable by humans, improving collaboration by making the robot's future actions more inferable from initial steps, demonstrated through experiments with physical robots.
Contribution
The paper proposes a novel approach to generate $t$-predictable plans that prioritize human interpretability over efficiency, enhancing collaborative robot-human interactions.
Findings
$t$-predictable plans improve human prediction accuracy.
The approach outperforms traditional efficiency-based planners.
User studies show increased subjective collaboration satisfaction.
Abstract
Collaboration requires coordination, and we coordinate by anticipating our teammates' future actions and adapting to their plan. In some cases, our teammates' actions early on can give us a clear idea of what the remainder of their plan is, i.e. what action sequence we should expect. In others, they might leave us less confident, or even lead us to the wrong conclusion. Our goal is for robot actions to fall in the first category: we want to enable robots to select their actions in such a way that human collaborators can easily use them to correctly anticipate what will follow. While previous work has focused on finding initial plans that convey a set goal, here we focus on finding two portions of a plan such that the initial portion conveys the final one. We introduce -\ACty{}: a measure that quantifies the accuracy and confidence with which human observers can predict the remaining…
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