Dear colleagues,
 
Our next BeNeRL Reinforcement Learning Seminar (June 13) is coming: 
Speaker: Nicklas Hansen (https://www.nicklashansen.com), PhD student from University of California San Diego.

Title: Data-Driven World Models for Robots
Date: June 13, 16.00-17.00 (CET)
Please find full details about the talk below this email and on the website of the seminar series: https://www.benerl.org/seminar-series
 
The goal of the online BeNeRL seminar series is to invite RL researchers (mostly advanced PhD or early postgraduate) to share their work. In addition, we invite the speakers to briefly share their experience with large-scale deep RL experiments, and their style/approach to get these to work.
 
We would be very glad if you forward this invitation within your group and to other colleagues that would be interested (also outside the BeNeRL region). Hope to see you on June 13!
 
Kind regards,
Zhao Yang & Thomas Moerland
Leiden University
 
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Upcoming talk: 
 
Date: June 13, 16.00-17.00 (CET)
Speaker: Nicklas Hansen (https://www.nicklashansen.com)

Title: Data-Driven World Models for Robots
Zoom: https://universiteitleiden.zoom.us/j/65411016557?pwd=MzlqcVhzVzUyZlJKTEE0Nk5uQkpEUT09
Abstract: Recent progress in AI can be attributed to the emergence of large models trained on large datasets. However, teaching AI agents to reliably interact with our physical world has proven challenging and, consequently, this new paradigm has not materialized as much in robotics as in related areas. In this talk, I will share my perspective on why it is challenging, what an agent that "understands" our physical world may look like, and how to build it. Concretely, I will discuss our work on TD-MPC, a highly data-driven approach to world models (models of the physical world) that can learn from diverse data, improve autonomously through real-world interaction, and scale with data and model size. I will discuss its algorithmic foundations, practical considerations, applications to diverse robot embodiments (manipulation, locomotion, humanoids) in both simulation and the real world, and conclude by sharing my perspective on future research directions.
Bio: Nicklas Hansen is a PhD student at University of California San Diego advised by Prof. Xiaolong Wang and Prof. Hao Su. His research focuses on developing generalist AI agents that learn from interaction with the physical and digital world. He has spent time at Meta AI (FAIR) and University of California Berkeley (BAIR), and received his BS and MS degrees from Technical University of Denmark. He is a recipient of the 2024 NVIDIA Graduate Fellowship, and his work has been featured at top venues in machine learning and robotics.