[Meetings][CrP] ICRA 2024 workshop on "Back to the Future: Robot Learning Going Probabilistic"
Jongseok Lee
The ICRA workshop on “Back to the Future: Robot Learning Going Probabilistic” welcomes submissions of papers about probabilistic robotics, robot learning and their intersections. In this year, the workshop will specially focus (but not limited to) on how probabilistic robotics can contribute or benefit from recent advances in foundational models.
Paper deadline: March 24, 2024 (AOE)
Acceptance Notification: April 8, 2024
Workshop date: May 13, 2024
Workshop website: https://probabilisticrobotics.github.io/
Submission website: https://openreview.net/group?id=IEEE.org/2024/ICRA/Workshop/Back_to_the_Future#tab-your-consoles
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.
Topics of InterestTopics of interest include but are not limited to:
Uncertainty in robot learning
Uncertainty in LLMs for robotics.
Uncertainty in visual-foundational models for robotics.
Uncertainty in diffusion models and neural radiance fields.
Differentiable Bayesian filtering and smoothing.
Probabilistic machine learning
Bayesian reinforcement learning.
Bayesian optimization.
Bayesian deep learning.
Bayesian inference.
Uncertainty quantification.
Out-of-distribution detection.
Applications of probabilistic robotics
Object recognition.
Simultaneous localization and mapping.
Planning under uncertainty.
Dynamics and control.
Reliable autonomy and system architectures.
Andreas Krause, ETH Zurich, Switzerland.
Andy Zeng, Google, USA.
Ayoung Kim, Seoul National University, Korea.
Peter Karkus, NVIDIA, USA.
Niko Sünderhauf, Queensland University of Technology, Australia.
Juho Lee, KAIST, Korea.
Sharon Yixuan Li, University of Wisconsin, USA.
Massashi Sugiyama, RIKEN and University of Tokyo, Japan.
Anthony Opipari, University of Michigan, USA.
Jana Pavlasek, University of Michigan, USA.
Jianxiang Feng, TU Munich and Agile Robots, Germany.
Jongseok Lee, DLR and KIT, Germany.
Yizhe Wu, University of Oxford, UK.
Rudolph Triebel, DLR and KIT, Germany.
Janis Postels, ETH Zurich, Switzerland.
Matthias Humt, DLR, Germany.
Thomas Power, University of Michigan, USA.
Chad Jenkins, University of Michigan, USA.
Fabio Ramos, University of Sydney and NVIDIA, Australia.
Tucker Hermans, University of Utah and NVIDIA, USA.
Workshop website: https://probabilisticrobotics.github.io/
For inquiries about the workshop, please contact probabilist...@dlr.de