2016
Lifelong learning for disturbance rejection on mobile robots
IROS 2016poster
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…