2022
A Framework for Robot Self-Assessment of Expected Task Performance
RA-L 2022
We propose a self-assessment framework which enables a robot to estimate how well it will be able to perform a known or possibly novel task. The robot simulates the task to generate a state distribution of possible outcomes and determines (1) the likelihood of overall success, (2) the most probable