학술논문

Avoiding Chatter in an Online Co-Learning Algorithm Predicting Human Intention
Document Type
Conference
Source
2019 IEEE 58th Conference on Decision and Control (CDC) Decision and Control (CDC), 2019 IEEE 58th Conference on. :6504-6509 Dec, 2019
Subject
Aerospace
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Robots
Prediction algorithms
Heuristic algorithms
Markov processes
Computational modeling
Upper bound
Limit-cycles
Language
ISSN
2576-2370
Abstract
Chatter can happen when an online learning algorithm is used by a robot to predict human intention while interacting with a human subject. When chatter happens, the learning algorithm continually changes its prediction, without reaching a constant prediction of human intention. Using the Rescorla-Wagner model for human learning, we analyze an expert based online learning algorithm and identify an invariant set in the state and parameter space where chatter will occur. Based on the chatter analysis, we also propose an improved expert based learning algorithm where the invariant set does not exist so that chatter can be avoided.