Robot Proficiency Self-Assessment Using Assumption-Alignment Tracking

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. A robot should be able to assess how well it can perform a task before, during, and after it has attempted...

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Bibliographic Details
Published in:IEEE transactions on robotics Vol. 39; no. 4; pp. 3279 - 3298
Main Authors: Cao, Xuan, Gautam, Alvika, Whiting, Tim, Smith, Skyler, Goodrich, Michael A., Crandall, Jacob W.
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Alignment
Assumption-alignment tracking (AAT)
autonomous robot system
Autonomous robots
Decision making
Generators
Markov chains
Performance evaluation
robot proficiency self-assessment
Robot sensing systems
Robots
Self assessment
Sensors
Task analysis
Tracking
Trajectory
ISSN:1552-3098, 1941-0468
Online Access:Get full text
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Summary: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. A robot should be able to assess how well it can perform a task before, during, and after it has attempted the task. How can autonomous robots be designed to self-assess their behavior? This article presents the assumption-alignment tracking (AAT) method for designing autonomous robots that can effectively evaluate their own performance. In AAT, the robot a) tracks the veracity of assumptions made by the robot's decision-making algorithms to measure how well these algorithms fit, or align with , its environment and hardware systems, and b) uses the measurement of alignment to assess the robot's ability to succeed at a given task based on its past experiences. The efficacy of AAT is illustrated through three case studies: a simulated robot navigating in a maze-based (discrete time) Markov chain environment, a simulated robot navigating in a continuous environment, and a real-world robot arranging blocks of different shapes and colors in a specific order on a table. Results show that AAT is able to accurately predict robot performance and, hence, determine robot proficiency in real time.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2023.3262187