LAUSR

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 failure location, and (3) the expected time to task completion. We evaluate the framework on the “FetchIt!” mobile manipulation challenge which requires the robot to fetch a variety of parts around a small enclosed arena. By comparing the simulated and actual task resulting state distributions, we show that the robot can effectively assess its expected performance which can be communicated to humans.