'Middle East Technical University, Faculty of Architecture'
Abstract
In this thesis, we reviewed the affordance concept for autonomous robot control and proposed that invariant features of objects that support a specific affordance can be learned. We used a physics-based robot simulator to study the reachability affordance on the simulated KURT3D robot model. We proposed that, through training, the values of each feature can be split into strips, which can then be used to detect the relevant features and their characteristics. Our analysis showed that it is possible to achieve higher prediction accuracy on the affordance support of novel objects by using only the relevant features. This is an important gain, since failures can have high costs in robotics and better prediction accuracy is desired.M.S. - Master of Scienc
