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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…
ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL Problems
Egor Cherepanov, Alexey K. Kovalev, Aleksandr I. Panov
Real-world robotic agents must act under partial observability and long horizons, where key cues may appear long before they affect decision making. However, most modern approaches…
Accelerating Transformers in Online RL
Daniil Zelezetsky, Alexey K. Kovalev, Aleksandr I. Panov
The appearance of transformer-based models in Reinforcement Learning (RL) has expanded the horizons of possibilities in robotics tasks, but it has simultaneously brought a wide ran…
Re:Frame -- Retrieving Experience From Associative Memory
Daniil Zelezetsky, Egor Cherepanov, Alexey K. Kovalev +1
Offline reinforcement learning (RL) often deals with suboptimal data when collecting large expert datasets is unavailable or impractical. This limitation makes it difficult for age…
AmbiK: Dataset of Ambiguous Tasks in Kitchen Environment
Anastasiia Ivanova, Eva Bakaeva, Zoya Volovikova +2
As a part of an embodied agent, Large Language Models (LLMs) are typically used for behavior planning given natural language instructions from the user. However, dealing with ambig…
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…