5 papers · 2 filters
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…
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…