Reusable Robot Learning

Markus Wulfmeier, University of Oxford

July 30, 2018


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 modularity, 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.


Markus is a postdoctoral research scientist at the Oxford Robotics Institute as well as a member of Oxford University’s New College. From September 2018 on, he will be joining Google DeepMind’s robotics efforts as research scientist. In 2017, he was a visiting scholar with the UC Berkeley Artificial Intelligence Research lab. The principal focus of his research is the development of approaches for increasing the efficiency of processes for providing supervision to guide autonomous systems with particular emphasis on modularity, transfer learning and learning from demonstration, work which was awarded as Best Student Paper at IROS16.

In early 2016, he additionally lead ORIs path planning software development for the presentation of a self driving prototype at the Shell Eco Marathon (SEM). This work has paved the way for to the introduction of a new autonomous challenge category at the SEM scheduled for 2018. Being in the field of robotics since 2010, he has been part of research efforts on space exploration robots, GPU-based simulations and robotic platforms for first responders as well as mobile autonomy at various research institutions including MIT, ETHZ, UC Berkeley and the University of Oxford.


Markus' publications are indexed at his google scholar site.