7 papers · 1 filter
MIKASA-Robo-VLA: Benchmarking Memory in VLA Models for Long-Horizon Manipulation
Egor Cherepanov, Nikita Kachaev, Aleksandr I. Panov +1
Vision-language-action policies often see only one or a few recent frames, which makes it difficult to evaluate how they use information that disappears during a task. We introduce…
Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models
Nikita Kachaev, Andrey Moskalenko, Matvey Skripkin +10
Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual kno…
VLA: On Recurrent Memory for Partially Observable Manipulation in VLA Models
Egor Cherepanov, Nikita Kachaev, Daniil Zelezetsky +6
Vision-language-action (VLA) models predict chunks of future actions from the current observation, an assumption that fails under partial observability, where decisions depend on i…
Don't Blind Your VLA: Aligning Visual Representations for OOD Generalization
Nikita Kachaev, Mikhail Kolosov, Daniil Zelezetsky +2
The growing success of Vision-Language-Action (VLA) models stems from the promise that pretrained Vision-Language Models (VLMs) can endow agents with transferable world knowledge a…
A New Perspective on Transformers in Online Reinforcement Learning for Continuous Control
Nikita Kachaev, Daniil Zelezetsky, Egor Cherepanov +2
Despite their effectiveness and popularity in offline or model-based reinforcement learning (RL), transformers remain underexplored in online model-free RL due to their sensitivity…
Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learning
Egor Cherepanov, Nikita Kachaev, Alexey K. Kovalev +1
Memory is crucial for enabling agents to tackle complex tasks with temporal and spatial dependencies. While many reinforcement learning (RL) algorithms incorporate memory, the fiel…
Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation
Egor Cherepanov, Nikita Kachaev, Artem Zholus +2
The incorporation of memory into agents is essential for numerous tasks within the domain of Reinforcement Learning (RL). In particular, memory is paramount for tasks that require…