Our work 'Action Conditional Recurrent Kalman Network' is accepted at #CoRL2020. Excited to share more details at the virtual conf. :) Thanks to Prof Gerhard Neuman(@alr_kit) and our collaborators Philipp Becker, Dieter Büchler, Marc Hanheide, L-CAS. arxiv.org/abs/2010.10201

Our work ‚Specializing Versatile Skill Libraries using Local Mixture of Experts‘ is accepted at #CoRL2021 🙂. openreview.net/forum?id=j3Rgu… Thanks to Prof. Neumann(@alr_kit), Zhou, Ge Li and Philipp Becker (Philipp Becker).

Onur's recent work on Versatile Skill Learning, published at Conference on Robot Learning 2021. Congrats! 🎉

Our work 'What Matters for Meta-Learning Vision Regression Tasks?' is accepted at #CVPR2022. Check it out if you are interested :) Codes and datasets will be available soon. Thanks to professor Gerhard Neumann and our collaborators Hanna Ziesche, Vien Ngo, Michael Volpp.

I am happy to share our recent work, “On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning”, ( openreview.net/pdf?id=UQXdQyo… ) published at TMLR. With Gerhard Neumann Let me summarize our key contributions in a short thread:

I'm super excited about this PhD work of mine, "Multi Time Scale World Models", which has been accepted to Neurips 2023 as a spotlight (Top 3% of all submitted papers). Details are in the thread below. (1/6)

Excited to share 4 accepted NeurIPS papers from our team. Amazing work and congratz to the team! Looking forward to seeing everyone in New Orleans :)

📢Interested in 6D pose estimation of novel objects w/o mesh models and gt-masks from clutter scenes? 🤖Check out our #CORL2023 paper "SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects"🎉 🌐sites.google.com/view/sa6d Bosch Center for Artificial Intelligence Autonomous Learning Robots Lab Conference on Robot Learning

🧩 How does #BayesianDeepLearning handle the wild terrain of real-world o.o.d. data? Novel work from our student Florian Seligmann with the #WILDS 🐾 datasets: Exploring transformers, finetuning and last-layer methods while ensembling it all! #NeurIPS2023 arxiv.org/abs/2306.12306

Curious how recent #BayesianDeepLearning methods perform on data beyond the training distribution? At #NeurIPS2023, there's large-scale survey on the #WILDS dataset, exploring fine-tuning, transformers, and deep ensembles in real-world scenarios. 📃➡️ bit.ly/3PXR25r