AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention

• URL: http://arxiv.org/abs/2511.18960v2
• Date: Tue, 02 Dec 2025 11:02:17 GMT
• Title: AVA-VLA: Improving Vision-Language-Action models with Active Visual Attention
• Abstract summary: Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in embodied AI tasks.<n>Current VLA models typically process dense visual inputs independently at each timestep.<n>This approach implicitly models the task as a Markov Decision Process (MDP)<n>We reformulate the problem from a Partially Observable Markov Decision Process (POMDP) perspective and propose a novel framework named AVA-VLA.
• Score: 24.446865513189433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in embodied AI tasks. However, existing VLA models, often built upon Vision-Language Models (VLMs), typically process dense visual inputs independently at each timestep. This approach implicitly models the task as a Markov Decision Process (MDP). However, this history-agnostic design is suboptimal for effective visual token processing in dynamic sequential decision-making, as it fails to leverage the context of history. To address this limitation, we reformulate the problem from a Partially Observable Markov Decision Process (POMDP) perspective and propose a novel framework named AVA-VLA. Inspired by the POMDP that the action generation should be conditioned on the belief state. AVA-VLA introduces Active Visual Attention (AVA) to dynamically modulate visual processing. It achieves this by leveraging the recurrent state, which is a neural approximation of the agent's belief state derived from the previous decision step. Specifically, the AVA module uses the recurrent state to compute the soft weights to actively process task-relevant visual tokens based on its historical context. Comprehensive evaluations demonstrate that AVA-VLA achieves state-of-the-art performance across popular robotic benchmarks, including LIBERO and CALVIN. Furthermore, real-world deployments on a dual-arm robot platform validate the framework's practical applicability and robust sim-to-real transferability.

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