A Method for Designing Autonomous Robots that Know Their Limits

Abstract

While the design of autonomous robots often emphasizes developing proficient robots, another important attribute of autonomous robot systems is their ability to evaluate their own proficiency and limitations. A robot should be able to assess how well it can perform a task before, during, and after it attempts the task. Thus, we consider the following question: How can we design autonomous robots that know their own limits? Toward this end, this paper presents an approach, called assumption-alignment tracking (AAT), for designing autonomous robots that can effectively evaluate their own limits. In AAT, the robot combines (a) measures of how well its decision-making algorithms align with its environment and hardware systems with (b) its past experiences to assess its ability to succeed at a given task. The effectiveness of AAT in assessing a robot's limits are illustrated in a robot navigation task.