Self-supervised learning of mobility affordances for vision-based navigation

cyvy Research Project

If the mobile service robots of the future, among them delivery robots and self-driving cars, were able to learn independently from their environments, they would also be capable of adapting to changes in their surroundings and thus move about more efficiently. In turn, this would eliminate the need for engineers to tune robots manually to their environments.


This research project addresses the question of how mobile robots can learn their driving capabilities in their environment (i.e. mobility affordances) in a self-supervised way. Jörg Stückler and his team will develop methods for learning motion models that will allow mobile robots to predict the effects of their actions. The scientists will develop a vision-based navigation approach that use learned models for motion planning. They will then evaluate this approach for the autonomous navigation of a mobile robot.