Dear colleagues,

Our next BeNeRL Reinforcement Learning Seminar (May 16) is coming: Speaker: Edward Hu (https://edwardshu.comhttps://edwardshu.com/), PhD student from the University of Pennsylvania.

Title: The Sensory Needs of Robot Learners Date: May 16, 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.https://www.benerl.org/seminar-seriesbenehttps://www.benerl.org/seminar-seriesrl.org/seminar-serieshttps://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 May 16!

Kind regards, Zhao Yang & Thomas Moerland Leiden University

——————————————————————

Upcoming talk:

Date: May 16, 16.00-17.00 (CET) Speaker: Edward Hu (https://edwardshu.comhttps://edwardshu.com/) Title: The Sensory Needs of Robot Learners Zoom: https://universiteitleiden.zoom.us/j/65411016557?pwd=MzlqcVhzVzUyZlJKTEE0Nk5... Abstract: What information does a robot need from the environment to efficiently learn new behaviors? Sensory streams and policy learning are intimately entangled. From current observation, agents learn models and compute feedback for improvement. Then, agents influence future observations through environmental interaction. I will present our recent findings in investigating the close-knit relationship between sensing and learning for robotics. This talk will cover a model-based RL approach that exploits additional sensors to improve policy search and an RL agent that learns interactive perception behavior to better estimate rewards. Overall, we find that paying careful attention to the sensory input streams of the RL process leads to large gains in performance. Bio: Edward Hu is a PhD student at the University of Pennsylvania and GRASP lab, advised by Dinesh Jayaraman. Edward is broadly interested in artificial intelligence, ranging from virtual agents to physical robots. As a result, his research spans reinforcement learning, perception, and robotics. His research has received multiple distinctions in robotics and machine learning venues like Best Paper Award at CoRL22, and spotlights at ICLR23 and ICLR24.