9 citations · 27 across the 23 of their papers we have counts for
7 papers · 1 filter
NinA: Normalizing Flows in Action. Training VLA Models with Normalizing Flows
Denis Tarasov, Alexander Nikulin, Ilya Zisman +5
Recent advances in Vision-Language-Action (VLA) models have established a two-component architecture, where a pre-trained Vision-Language Model (VLM) encodes visual observations an…
cadrille: Multi-modal CAD Reconstruction with Reinforcement Learning
Maksim Kolodiazhnyi, Denis Tarasov, Dmitrii Zhemchuzhnikov +6
Computer-Aided Design (CAD) plays a central role in engineering and manufacturing, making it possible to create precise and editable 3D models. Using a variety of sensor or user-pr…
Zero-Shot Adaptation of Behavioral Foundation Models to Unseen Dynamics
Maksim Bobrin, Ilya Zisman, Alexander Nikulin +2
Behavioral Foundation Models (BFMs) proved successful in producing policies for arbitrary tasks in a zero-shot manner, requiring no test-time training or task-specific fine-tuning.…
Yes, Q-learning Helps Offline In-Context RL
Denis Tarasov, Alexander Nikulin, Ilya Zisman +6
Existing offline in-context reinforcement learning (ICRL) methods have predominantly relied on supervised training objectives, which are known to have limitations in offline RL set…
Object-Centric Latent Action Learning
Albina Klepach, Alexander Nikulin, Ilya Zisman +6
Leveraging vast amounts of unlabeled internet video data for embodied AI is currently bottlenecked by the lack of action labels and the presence of action-correlated visual distrac…
Latent Action Learning Requires Supervision in the Presence of Distractors
Alexander Nikulin, Ilya Zisman, Denis Tarasov +4
Recently, latent action learning, pioneered by Latent Action Policies (LAPO), have shown remarkable pre-training efficiency on observation-only data, offering potential for leverag…
Vintix: Action Model via In-Context Reinforcement Learning
Andrey Polubarov, Nikita Lyubaykin, Alexander Derevyagin +4
In-Context Reinforcement Learning (ICRL) represents a promising paradigm for developing generalist agents that learn at inference time through trial-and-error interactions, analogo…