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Andrii Zadaianchuk

12 accepted papers

2026

Probing Newtonian Mechanics in Video Generative Models with Real Physical Systems

Antonios Tragoudaras, Daniil Cherniavskii, Chenyu Zhang, Antonis Vozikis, Thijmen Nijdam, Derck Prinzhorn +4
ICML 2026poster

Recent advances in image and video generation raise hopes that these models possess world modeling capabilities—the ability to generate realistic, physically plausible videos. This could revolutionize applications in robotics, autonomous driving, and scientific simulation. However, before treating t…

Cited by 0Source ↗Scholar ↗
2025

CTRL-O: Language-Controllable Object-Centric Visual Representation Learning

Aniket Didolkar, Andrii Zadaianchuk, Rabiul Awal, Maximilian Seitzer, Efstratios Gavves, Aishwarya Agrawal
CVPR 2025poster

Object-centric representation learning aims to decompose visual scenes into fixed-size vectors called "slots" or "object files", where each slot captures a distinct object. Current state-of-the-art object-centric models have shown remarkable success in object discovery in diverse domains including c…

Cited by 2Source ↗PDF ↗Scholar ↗
2025

Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination

Leonardo Barcellona, Andrii Zadaianchuk, Davide Allegro, Samuele Papa, Stefano Ghidoni, Efstratios Gavves
ICLR 2025poster

A world model provides an agent with a representation of its environment, enabling it to predict the causal consequences of its actions. Current world models typically cannot directly and explicitly imitate the actual environment in front of a robot, often resulting in unrealistic behaviors and hall…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2025

On the Transfer of Object-Centric Representation Learning

Aniket Rajiv Didolkar, Andrii Zadaianchuk, Anirudh Goyal, Michael Curtis Mozer, Yoshua Bengio, Georg Martius +1
ICLR 2025poster

The goal of object-centric representation learning is to decompose visual scenes into a structured representation that isolates the entities into individual vectors. Recent successes have shown that object-centric representation learning can be scaled to real-world scenes by utilizing features from…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models

Cansu Sancaktar, Christian Gumbsch, Andrii Zadaianchuk, Pavel Kolev, Georg Martius
ICML 2025poster

Exploration is a cornerstone of reinforcement learning (RL). Intrinsic motivation attempts to decouple exploration from external, task-based rewards. However, established approaches to intrinsic motivation that follow general principles such as information gain, often only uncover low-level interact…

Cited by 2Source ↗PDF ↗Scholar ↗
2025

Temporally Consistent Object-Centric Learning by Contrasting Slots

Anna Manasyan, Maximilian Seitzer, Filip Radovic, Georg Martius, Andrii Zadaianchuk
CVPR 2025poster

Unsupervised object-centric learning from videos is a promising approach to extract structured representations from large, unlabeled collections of videos. To support downstream tasks like autonomous control, these representations must be both compositional and temporally consistent. Existing approa…

Cited by 1Source ↗PDF ↗Scholar ↗
2023

Bridging the Gap to Real-World Object-Centric Learning

Maximilian Seitzer, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel +5
ICLR 2023poster

Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decomposition in an unsupervised way has become an important line of research. However, current methods are restricted to simula…

Cited by 144Source ↗PDF ↗Scholar ↗
2023

Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities

Andrii Zadaianchuk, Maximilian Seitzer, Georg Martius
NeurIPS 2023poster

Unsupervised video-based object-centric learning is a promising avenue to learn structured representations from large, unlabeled video collections, but previous approaches have only managed to scale to real-world datasets in restricted domains. Recently, it was shown that the reconstruction of pre-t…

Cited by 42Source ↗PDF ↗code ↗Scholar ↗
2023

Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations

Andrii Zadaianchuk, Matthaeus Kleindessner, Yi Zhu, Francesco Locatello, Thomas Brox
ICLR 2023top-25%

In this paper, we show that recent advances in self-supervised representation learning enable unsupervised object discovery and semantic segmentation with a performance that matches the state of the field on supervised semantic segmentation 10 years ago. We propose a methodology based on unsupervise…

Cited by 54Source ↗PDF ↗code ↗Scholar ↗
2021

Self-supervised Reinforcement Learning with Independently Controllable Subgoals

Andrii Zadaianchuk, Georg Martius, Fanny Yang
CoRL 2021poster

To successfully tackle challenging manipulation tasks, autonomous agents must learn a diverse set of skills and how to combine them. Recently, self-supervised agents that set their own abstract goals by exploiting the discovered structure in the environment were shown to perform well on many differe…

Cited by 27Source ↗Scholar ↗
2021

Self-supervised Visual Reinforcement Learning with Object-centric Representations

Andrii Zadaianchuk, Maximilian Seitzer, Georg Martius
ICLR 2021spotlight

Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. However, acquiring these skills using only a stream of high-dimensional, unstructured, and unlabeled observations is a tricky challenge for any autonomous agent. Previous methods have us…

Cited by 54Source ↗PDF ↗code ↗Scholar ↗
2020

A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models

Diego Agudelo-España, Andrii Zadaianchuk, Philippe Wenk, Aditya Garg, Joel Akpo, Felix Grimminger +8
ICRA 2020poster

In the context of model-based reinforcement learning and control, a large number of methods for learning system dynamics have been proposed in recent years. The purpose of these learned models is to synthesize new control policies. An important open question is how robust current dynamics-learning m…

Cited by 10Source ↗Scholar ↗