BeNeRL Seminar Series


The BeNeRL Seminar Series are monthly online talks by RL researchers from all over the world. The intention is to primarily give a platform to advanced PhD students and early career researchers to 1) display their work and 2) share their practical RL experience (i.e., how do you manage large-scale RL experiments as a new researcher in the field, a topic that is often skipped in talks). We maintain a summary of the main advice on experimentation.


The seminar is online and takes place on every second Thursday of the month, 16.00-17.00 (CET)
(unless there is a conflict with an important machine learning conference, when we try to shift by one week)


Upcoming Speaker: Nicklas Hansen (UC San Diego)
(ZOOM LINK- CLICK HERE )

Date: June 13, 16.00-17.00 (CET)

Title: Data-Driven World Models for Robots

Link: Zoom Link - Click Here

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.

Schedule


Talks are always online and take place between 16.00-17.00 (CET).




2023




2024






Summer break




If you have any questions about the seminar series, feel free to contact:


Zhao Yang: z.yang(at)liacs.leidenuniv.nl


Thomas Moerland