Autonomous Learning Robots Lab
@alr_lab_kit
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The Autonomous Learning Robots Lab @KITKarlsruhe develops methods on the intersection of machine learning and robotics. Lead by Prof. @geri_neumann
Joined December 2021
🧠 Onur Celik (@onclk_ ) presents his ICML publication “Acquiring Diverse Skills using Curriculum RL with Mixture of Experts” at the ARLET workshop.
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🔍 Philipp Becker (@philippb06) will also be at ARLET, presenting “Combining Reconstruction and Contrastive Methods for Multimodal Representations in RL” and his preliminary work on “KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty.”
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Excited to announce that today we will present four papers at the ICML workshops : 📐 @NiklasFreymuth will showcase “Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations” @ the AI for Science Workshop. Stay tuned! #ICML2024 #MachineLearning
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Having great pleasure from presenting our work in temporally correlated RL ( https://t.co/PARTcXzWGY) in #ICLR2024 and enjoying a lot of fun in #Vienna
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🧩 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
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📢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"🎉 🌐 https://t.co/M183ag5iF0
@Bosch_AI @alr_lab_kit @corl_conf
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Feeling the stress of slow simulations?⏰Let Adaptive Swarm Mesh Refinement (ASMR) soothe your computational woes! Our method produces high-quality mesh refinements that offer up to 100x speedup.🎧📊 https://t.co/nfxW1qd3Zm
https://t.co/s13qaa4wps Catch us at #NeurIPS2023!
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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 :)
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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)
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I am happy to share our recent work, “On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning”, ( https://t.co/PWazDxVfBg ) published at TMLR. With @geri_neumann Let me summarize our key contributions in a short thread:
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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 @philippb06, @dtrbchlr, @MarcHanheide, @LCAS_UoL. https://t.co/pyAdnQwymy
arxiv.org
Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for sophisticated robots such as robots driven by hydraulics, artificial muscles, or robots...
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Our work ‚Specializing Versatile Skill Libraries using Local Mixture of Experts‘ is accepted at #CoRL2021 🙂. https://t.co/Vmw8CRhjJL Thanks to Prof. Neumann(@alr_kit), Zhou, Ge Li and Philipp Becker (@philippb06).
openreview.net
A long-cherished vision in robotics is to equip robots with skills that match the versatility and precision of humans. For example, when playing table tennis, a robot should be capable of returning...
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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 @geri_neumann and our collaborators Hanna Ziesche, @VienNgo4, @volppmichael.
What Matters For Meta-Learning Vision Regression Tasks? https://t.co/v0jtVTMatG by @gaobaoding et al. #Statistics #Estimator
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Onur's recent work on Versatile Skill Learning, published at @corl_conf 2021. Congrats! 🎉
Our work ‚Specializing Versatile Skill Libraries using Local Mixture of Experts‘ is accepted at #CoRL2021 🙂. https://t.co/Vmw8CRhjJL Thanks to Prof. Neumann(@alr_kit), Zhou, Ge Li and Philipp Becker (@philippb06).
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