ICRA 2024 Workshop

Back to the Future: Robot Learning Going Probabilistic
Date: TBD | Room: TBD

Speakers

Andreas Krause
Andy Zeng
Ayoung Kim
Peter Karkus
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
Abstract: ↵

TBD

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
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

Schedule

Time Speaker Session/Title
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
10:30-11:00   Coffee break
11:00-11:30 Ayoung Kim Deep learning with a graph SLAM for robot manipulation
11:30-12:00 Peter Karkus  
12:00-13:00   Lunch break
13:00-13:30 Sharon Li How to Detect Out-of-Distribution Data in the Wild? Challenges, Progress, and Opportunities
13:30-14:00 Niko Sünderhauf  
14:00-15:00   Contributed talks from selected paper
15:00-15:30   Interactive poster session
15:00-15:30   Coffee break
15:30-16:00 Andy Zeng From Words to Actions
16:00-17:00   Panel discussion

Accepted Papers

About

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.

Committees

Organizing Committee

Anthony Opipari
Jana Pavlasek
Jianxiang Feng
Jongseok Lee
Yizhe Wu
Rudolph Triebel
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)

Scientific Committee

Janis Postels
Matthias Humt
Thomas Power
Chad Jenkins
Tucker Hermans
Fabio Ramos
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)

Endorsements

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.

Contact

Should you have any questions, please do not hesitate to contact the organizing committee.