Vortrag: Scalable and Efficient Robot Learning
Dr. Markus Wulfmeier, Oxford Robotics Institute
Automation and applications of robots in various fields bear the promise of reducing expenses as well as time requirements for production, logistics, transportation, and others. The first step towards automation included writing down our own rules and intuitions about how machines should solve tasks: programming. Machine learning enables us to generate rules which are too complex to be manually formulated by training highly flexible models based on large datasets. Our efforts have been shifted from rule design to the collection, cleaning, and annotation of data. To overcome increasing time demands for larger and larger datasets, we rely on methods from fields such as transfer learning, domain adaptation, learning from demonstration and reinforcement learning. In this talk, I will summarise some of our recent work from the Oxford Robotics Institute (University of Oxford) and the Berkeley AI Research lab (UC Berkeley) aiming at conceptualising the current challenges as well as the potentials for increasing the efficiency of humans to increase the efficiency of robotic automation.
9 November 2018, 14:00
MZ2, Erdgeschoss, Appelstr. 9A