Most robot systems have parameters which require tuning in order to work properly. Tuning these parameters autonomously is important for adapting to changes within a robot system or in its environment. Changes affecting robots might include degradation of equipment such as cameras and light sensors gradually losing intensity, antennae being damaged, or gyroscopes losing accuracy. Recently Nomad was deployed in Antarctica, and the meteorite-finding vision software which worked perfectly in the lab took several weeks of adjustment before working in the field. Handling varying environments is important because good parameter settings learned for one situation may not work at all in another.
Environmental variations can affect perception in other robots as well.
Stereo vision algorithms rely on visual texture to produce accurate depth measurements, so in a sandy landscape with few rocks a vision algorithm will need different adjustments than are appropriate for a rocky environment. Likewise camera settings should be adjusted differently when looking nearly into the sun than when looking away from the sun, or into a region of shadow. Perhaps most importantly, there may be completely unanticipated relationships such as temperature variations affecting sensor performance. A learning system which does not take environmental changes into account and assumes that there is one unique set of best parameters will be forced to continually re-learn good settings as it moves from one situation to another and back again. This project will advance the state of the art in automatic parameter optimization primarily in its use of sensed environmental features to learn which situations require different parameter settings for best performance.