Christian Gumbsch

Postdoc @UvA_Amsterdam | world models and sensorimotor abstractions |👾🤖🧠

[Christian Gumbsch @cgumbsch]

World models are key for developing adaptive agents. In our #ICLR2024 spotlight we present THICK: an algorithm to learn hierarchical world models with versatile temporal abstractions. And we show how they can enhance model-based RL or planning…
📜 https://t.co/9SVk5TEis5
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[Andrii Zadaianchuk 🇺🇦 @ZadaianchukML]

🚀 Announcing #RSS2026 workshop: Robot World Models 🌐🤖🐨🌐 July 17, 2026 in 🇦🇺!
We're bringing together researchers building the next generation of WM for robots — models robots can act in: controllable, long-horizon & grounded in real dynamics. 🔗 https://t.co/wMRyAcPzlP

[Andrii Zadaianchuk 🇺🇦 @ZadaianchukML]

🤖 🌍 We have extended the submission to our CoRL (@corl_conf) workshop about Robot World Models for one more week! Use your chance to submit! 🌏🤖

❗️Both 1-page abstracts of published works and 4-page novel works in progress are welcome!

📡 Details: https://t.co/XjeFO3YnBn

[Max Seitzer @maxseitzer]

Introducing DINOv3 🦕🦕🦕

A SotA-enabling vision foundation model, trained with pure self-supervised learning (SSL) at scale.
High quality dense features, combining unprecedented semantic and geometric scene understanding.

Three reasons why this matters…

[Andrii Zadaianchuk 🇺🇦 @ZadaianchukML]

Are you working on real-to-sim, sim-to-real, learning world models, or using physics-based simulators? There are two weeks left until the submission deadline for our CoRL workshop, Learning to Simulate Robot Worlds. More details here:
🔗https://t.co/XjeFO3YnBn

[Andrii Zadaianchuk 🇺🇦 @ZadaianchukML]

🚀 We’re excited to announce our #CoRL2025 workshop: Learning to Simulate Robot Worlds
Spanning high-fidelity simulators, digital twins, and learned world models - our goal is to unite communities to push robot learning forward 🤖🌐
🔗 https://t.co/AjKxrN5DId

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[Georg Martius @GMartius]

@svlevine was just presenting in the Exploration in AI @ #ICML2025 and promoted that exploration needs to be grounded, and that VLMs are a good source ;-) Check our paper below
👇

[Thomas Rupf @th_rupf]

Zero-shot imitation from just a single sparse demonstration is hard. Goal-conditioned methods tend to “greedily" move from one state to the next and lose the big picture.

We're presenting an alternative approach on Tuesday at #ICML2025.

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[Marco Bagatella @mar_baga]

When multiple tasks need improvements, fine-tuning a generalist policy becomes tricky. How do we allocate a demonstration budget across a set of tasks of varied difficulty and familiarity?

We are presenting a possible solution at ICML on Wednesday!

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[Cansu Sancaktar @CcansuSancaktar]

✨Introducing SENSEI✨ We bring semantically meaningful exploration to model-based RL using VLMs.

With intrinsic rewards for novel yet useful behaviors, SENSEI showcases strong exploration in MiniHack, Pokémon Red & Robodesk.

Accepted at ICML 2025🎉
Joint work with @cgumbsch
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[Andrii Zadaianchuk 🇺🇦 @ZadaianchukML]

How to represent dynamic real-world data both consistently and efficiently, while reflecting the compositional object-centric structure of the world?

Contrast your slots!

...with our new SlotContrast method(🚀#CVPR2025 Oral🚀)!

🌐website: https://t.co/UXeYp8qyAy

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[Asya Achimova @AAchimova]

#Context plays a critical role not only in interpreting language but many other cognitive processes. In a new paper, we propose that context-sensitivity is a core feature of human memory that enables flexible planning, #generalization, and decision making https://t.co/LigJbVD4Dc

[Marin Vlastelica 🤖🎸 @vlastelicap]

Were you ever wondering why there are little-to-no diverse offline imitation learning algorithms 🤔? Then we've got something 4 you! Our paper, "Offline Diversity Maximization under Imitation Constraints" is being presented today at RLC2024 🎇! 🧵

https://t.co/NTOedhW9vD

[IMOL Workshop | NeurIPS 2024 @IMOLNeurIPS2024]

🚨We're back!🚨 Excited to announce The 2nd IMOL Workshop at #neurips2024!

Send us your 📜newest work📜on learning & exploration in artificial and biological agents. #CFP at https://t.co/6KDwrtx36b

🔄Please share widely & stay tuned for more programming announcements!

[Núria Armengol @NriaArmengol2]

Tired of causally confused agents when learning from offline datasets?
We propose 🚣🏼‍♀️CAIAC🚣🏼‍♀️, a method for counterfactual data augmentation to improve the robustness of offline learning agents against extreme distributional shifts at test time. 🧵

[Pietro Mazzaglia @pietromazzaglia]

🚨Introducing GenRL!
An embodied AI agent that learns multimodal foundation world models 🌍

By connecting the multimodal knowledge of foundation models with the embodied knowledge of world models for RL, GenRL enables turning vision and language prompts into actions!

[Christian Gumbsch @cgumbsch]

For more details check out our paper or our code.

Paper 📜: https://t.co/9SVk5TEis5
Code 🐍: https://t.co/UqFIbp48Lt

Many thanks to @nsajidt, @GMartius, and @mvbutz!

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[Christian Gumbsch @cgumbsch]

World models are key for developing adaptive agents. In our #ICLR2024 spotlight we present THICK: an algorithm to learn hierarchical world models with versatile temporal abstractions. And we show how they can enhance model-based RL or planning…
📜 https://t.co/9SVk5TEis5
1/🧵

[Christian Gumbsch @cgumbsch]

THICK PlaNet 🪐: We can also plan directly with our model. In THICK PlaNet we first plan on the high level with MCTS and then we search for low-level actions to follow this plan. This is useful for long-horizon or hierarchical tasks and sparse rewards.
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