Intelligent Robot Learning Laboratory (IRL Lab) Lifelong Learning for Heterogenous Robot Teams

By: Gabriel V. de la Cruz Jr., James M. Irwin, and Matthew E. Taylor

Undergraduates: Brandon Kallaher (WSU)

This is a joint project of WSU, University of Pennsylvania and Olin College. This project is about developing transfer learning methods that enable teams of heterogenous agents to rapidly adapt control and coordination policies to new scenarios. Our approach uses a combination of lifelong transfer learning and autonomous instruction to support continual transfer among heterogeneous agents and across diverse tasks. The resulting multi-agent system will accumulate transferrable knowledge over consecutive tasks, enabling the transfer learning process to improve overtime and the system to become increasingly versatile. We will apply these methods to sequential decision making (SDM) tasks in dynamic environments with aerial and ground robots. [1, 2]

[1] [pdf] David Isele, José Marcio Luna, Eric Eaton, Gabriel V. de la Cruz Jr., James Irwin, Brandon Kallaher, and Matthew E. Taylor. Lifelong Learning for Disturbance Rejection on Mobile Robots. In Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2016. 48% acceptance rate
[Bibtex]
@inproceedings{2016IROS-Isele,
author={Isele, David and Luna, Jos\'e Marcio and Eaton, Eric and de la Cruz, Jr., Gabriel V. and Irwin, James and Kallaher, Brandon and Taylor, Matthew E.},
title={{Lifelong Learning for Disturbance Rejection on Mobile Robots}},
booktitle={{Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems ({IROS})}},
month={October},
year={2016},
note={48% acceptance rate},
video={https://youtu.be/u7pkhLx0FQ0},
bib2html_pubtype={Refereed Conference},
abstract={No two robots are exactly the same—even for a given model of robot, different units will require slightly different controllers. Furthermore, because robots change and degrade over time, a controller will need to change over time to remain optimal. This paper leverages lifelong learning in order to learn controllers for different robots. In particular, we show that by learning a set of control policies over robots with different (unknown) motion models, we can quickly adapt to changes in the robot, or learn a controller for a new robot with a unique set of disturbances. Furthermore, the approach is completely model-free, allowing us to apply this method to robots that have not, or cannot, be fully modeled.}
}
[2] [pdf] David Isele, José Marcio Luna, Eric Eaton, Gabriel V. de la Cruz Jr., James Irwin, Brandon Kallaher, and Matthew E. Taylor. Work in Progress: Lifelong Learning for Disturbance Rejection on Mobile Robots. In Proceedings of the Adaptive Learning Agents (ALA) workshop (at AAMAS), Singapore, May 2016.
[Bibtex]
@inproceedings{2016ALA-Isele,
author={Isele, David and Luna, Jos\'e Marcio and Eaton, Eric and de la Cruz, Jr., Gabriel V. and Irwin, James and Kallaher, Brandon and Taylor, Matthew E.},
title={{Work in Progress: Lifelong Learning for Disturbance Rejection on Mobile Robots}},
booktitle={{Proceedings of the Adaptive Learning Agents ({ALA}) workshop (at {AAMAS})}},
year={2016},
address={Singapore},
month={May},
abstract = {No two robots are exactly the same — even for a given model of robot, different units will require slightly different controllers. Furthermore, because robots change and degrade over time, a controller will need to change over time to remain optimal. This paper leverages lifelong learning in order to learn controllers for different robots. In particular, we show that by learning a set of control policies over robots with different (unknown) motion models, we can quickly adapt to changes in the robot, or learn a controller for a new robot with a unique set of disturbances. Further, the approach is completely model-free, allowing us to apply this method to robots that have not, or cannot, be fully modeled. These preliminary results are an initial step towards learning robust fault-tolerant control for arbitrary robots.}
}