Dr. Oier Mees (University of Freiburg) talks on Interactive Language Instructable Robot Learning at the the Physics Department

VERANSTALTUNGEN | | KI- und Robotik-Talks

Despite considerable progress in robot learning, and contrary to the expectations of the general public, the vast majority of robots deployed out in the real world today continue to remain restricted to a narrow set of preprogrammed behaviors for specific tasks in controlled industrial settings. As robots become ubiquitous across human-centred environments, the need for “generalist” robots grows: how can we scale robot learning systems to autonomously acquire general-purpose knowledge that allows them to perform a wide range of everyday tasks in unstructured environments based on arbitrary instructions from the user? In my work, I have focused on addressing the challenging problem of relating human language to a robot’s perceptions and actions by introducing techniques that leverage self-supervision and structural priors from uncurated data to enable sample-efficient learning of language-conditioned manipulation and navigation tasks.

Interactive Language Instructable Robot Learning
Dr. Oier Mees (University of Freiburg) | TUM-Robotics Talks | 13. June 2023, 15:00 – 16:15 CET

Location: Room N2409, Theresienstr. 90. See map


Dr. Oier Mees

Oier received his PhD in Computer Science in 2023 from the Freiburg University supervised by Prof. Dr. Wolfram Burgard. His research focuses on robot manipulation and deep learning, to enable machines to intelligently interact with both the physical world and humans, and improve themselves over time. Oier is particularly interested in scaling robot learning systems to autonomously acquire general-purpose knowledge that allows them to compose long-horizon tasks by following unconstrained language instructions. Oier has recently received the 2023 AI Newcomer award by the German Federal Ministry of Education and Research and the German Informatics Society. His work CALVIN: A Benchmark for Language-conditioned Policy Learning for Long-horizon Robot Manipulation Tasks was recognized as the 2022 IEEE Robotics and Automation Letters Best Paper and the work Grounding Language with Visual Affordances over Unstructured Data has been nominated for the Best Paper Award in Robot Learning at ICRA 2023.