5 papers
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…
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…
Mediated Multi-Agent Reinforcement Learning
Dmitry Ivanov, Ilya Zisman, Kirill Chernyshev
The majority of Multi-Agent Reinforcement Learning (MARL) literature equates the cooperation of self-interested agents in mixed environments to the problem of social welfare maximi…