Multilayer General Value Functions for Robotic Prediction and Control
Full Text: Sherstan_2014_AIROBOT.pdf
Predictions are a key component to intelligence and necessary for accurate motor control. In reinforcement learning, such predictions can be made through general value functions (GVFs). This paper introduces prosthetic arms as a domain for artificial intelligence and discusses the role that predictions play in prosthetic limb control. We explore the use of multilayer predictions, that is, predictions based on predictions, using robotic and simulation experiments. From these experiments two observations are made. The first is that compound predictions based on GVFs are viable in a robotic setting. The second, is that strong GVF predictors can be built from weaker ones with different input and target signals, similar to boosting. Finally, we theorize how such topologies might be used in transfer learning and in the simultaneous control of multiple actuators. Our approach to integrating machine intelligence with robotics has the potential to directly improve the real-world performance of bionic limbs.
Citation
C. Sherstan, P. Pilarski. "Multilayer General Value Functions for Robotic Prediction and Control". International Conference on Intelligent Robots and Systems, September 2014.| Keywords: | |
| Category: | In Conference |
| Web Links: | UofA |
BibTeX
@incollection{Sherstan+Pilarski:IROS14,
author = {Craig Sherstan and Patrick M. Pilarski},
title = {Multilayer General Value Functions for Robotic Prediction and
Control},
booktitle = {International Conference on Intelligent Robots and Systems},
year = 2014,
}Last Updated: January 19, 2021Submitted by Sabina P
