|Andreas Krause||Andy Zeng||Ayoung Kim||Peter Karkus|
|ETH Zürich & LatticeFlow (Switzerland)||Google DeepMind (United Kingdom)||Seoul National University (Korea)||NVIDIA (USA)|
|Talk: Safe and Efficient Exploration in Bayesian Model-based Reinforcement Learning
|Talk: From Words to Actions||Talk: Deep learning with a graph SLAM for robot manipulation||Talk: End-to-end yet Modular Probabilistic Robot Learning|
|Niko Sünderhauf||Juho Lee||Sharon Yixuan Li||Masashi Sugiyama|
|Queensland University of Technology (Australia)||KAIST (Korea)||University of Wisconsin (USA)||RIKEN and University of Tokyo|
|Talk: Predictive Uncertainty Neural Radiance Fields for Robotics||Talk: Scaling Bayesian deep learning for fast and accurate inference||Talk: How to Detect Out-of-Distribution Data in the Wild? Challenges, Progress, and Opportunities||Talk: Learning under Continuous Distribution Shifts|
|08:45-09:00||Welcome message from organisers|
|09:00-09:30||Andreas Krause||Safe and Efficient Exploration in Bayesian Model-based Reinforcement Learning|
|09:30-10:00||Juho Lee||Scaling Bayesian deep learning for fast and accurate inference|
|10:00-10:30||Masashi Sugiyama||Learning under Continuous Distribution Shifts|
|11:00-11:30||Ayoung Kim||Deep learning with a graph SLAM for robot manipulation|
|13:00-13:30||Sharon Li||How to Detect Out-of-Distribution Data in the Wild? Challenges, Progress, and Opportunities|
|14:00-15:00||Contributed talks from selected paper|
|15:00-15:30||Interactive poster session|
|15:30-16:00||Andy Zeng||From Words to Actions|
Probabilistic robotics, a vibrant field that has gained enormous popularity since its inception, provides a compelling paradigm for autonomous robots to contend with the complex real world. Probabilistic representations yield beneficial properties for trustworthy learning-enabled robots deployed in the real world, e.g., uncertainty estimation, ways to elegantly handle incomplete data and the unifying perspective on perception, control and learning. On the other hand, recent advances in deep learning have dramatically improved the suitability and performance of robot learning, e.g., large language models (LLMs), visual foundational models, and Neural Radiance Fields (NeRFs), to name a few. Though there have been advances in pursuing the probabilistic extension of these concepts in recent years, many core challenges associated with real-world deployment remain unsolved.
In light of the above, this workshop aims to provide a forum to bring together robotic and machine learning researchers as well as industry experts with experience in developing probabilistic methods that dovetail with robot learning. In order to facilitate breakthrough research in these areas, the discussions will be centred on past achievements, current requirements, urgent challenges and future directions to enable promising applications.
|Anthony Opipari||Jana Pavlasek||Jianxiang Feng||Jongseok Lee||Yizhe Wu||Rudolph Triebel|
|University of Michigan (USA)||University of Michigan (USA)||TU Munich (Germany)||DLR (Germany)||University of Oxford (United Kingdom)||DLR & Karlsruhe Institute of Technology (Germany)|
|Janis Postels||Matthias Humt||Thomas Power||Chad Jenkins||Tucker Hermans||Fabio Ramos|
|ETH Zurich (Switzerland)||DLR & TU Munich (Germany)||University of Michigan (USA)||University of Michigan (USA)||University of Utah & NVIDIA (USA)||University of Sydney & NVIDIA
(Australia & USA)
This workshop is proud to be endorsed by the IEEE RAS Technical Committees for Computer and Robot Vision, and Autonomous Ground Vehicles and Intelligent Transportation Systems.
Should you have any questions, please do not hesitate to contact the organizing committee.