We propose a self-assessment framework which enables a robot to estimate how well it will perform a known or novel task. The robot simulates the task by sampling performance distributions to generate a state distribution of possible outcomes and determines 1) the likelihood of success, 2) the most probable failure location, and 3) the expected time to task completion.
@article{frascascheutz2022ral,
title={A Framework for Robot Self-Assessment of Expected Task Performance},
author={Tyler Frasca and Matthias Scheutz},
year={2022},
journal={IEEE Robotics and Automation Letters},
volume={7},
pages={12523--12530}
url={https://hrilab.tufts.edu/publications/frascascheutz2022ral.pdf}
doi={10.1109/LRA.2022.3219024}
}