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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…
ALER: Adaptive Learnable Experience Rewriting for Reinforcement Learning
Oleg Shchendrigin, Egor Cherepanov, Aleksandr I. Panov +1
In partially observable reinforcement learning (RL), a later observation can make stored information obsolete or change what it implies for the next decision. Memory architectures…
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…
Memory Retention Is Not Enough to Master Memory Tasks in Reinforcement Learning
Oleg Shchendrigin, Egor Cherepanov, Alexey K. Kovalev +1
Effective decision-making in the real world depends on memory that is both stable and adaptive: environments change over time, and agents must retain relevant information over long…
KAGE-Bench: Fast Known-Axis Visual Generalization Evaluation for Reinforcement Learning
Egor Cherepanov, Daniil Zelezetsky, Alexey K. Kovalev +1
Pixel-based reinforcement learning agents often fail under purely visual distribution shift even when latent dynamics and rewards are unchanged, but existing benchmarks entangle mu…
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…
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…
Object-Centric Learning with Slot Mixture Module
Daniil Kirilenko, Vitaliy Vorobyov, Alexey K. Kovalev +1
Object-centric architectures usually apply a differentiable module to the entire feature map to decompose it into sets of entity representations called slots. Some of these methods…
Recurrent Action Transformer with Memory
Egor Cherepanov, Alexey Staroverov, Alexey K. Kovalev +1
Transformers have become increasingly popular in offline reinforcement learning (RL) due to their ability to treat agent trajectories as sequences, reframing policy learning as a s…