arXiv:2511.18960v4 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have shown remarkable progress in embodied tasks recently, but most methods process visual observations independently at each timestep. This history-agnostic design treats robot manipulation as a Markov Decision Process, even though real-world robotic control is inherently partially observable and requires reasoning over past interactions. To address this mismatch, we reformulate VLA policy learning from a Partially Observable Markov Decision Process perspective and propose AVA-VLA, a framework that conditi
The rapid advancement of AI in simulated and real-world environments is pushing the boundaries of autonomous systems, necessitating more sophisticated control mechanisms for complex tasks.
Improving how AI models process historical context and operate in partially observable environments is crucial for developing truly robust and general-purpose autonomous agents, particularly in robotics.
This research introduces a framework that allows Vision-Language-Action models to reason more effectively over past interactions, moving beyond history-agnostic control.
- · Robotics companies
- · AI research institutions
- · Logistics and manufacturing automation
- · Companies relying on simplistic AI for complex tasks
More capable and reliable autonomous robots emerge for various applications.
Reduced need for human intervention in complex robotic operations, leading to efficiency gains.
Accelerated development of general-purpose humanoid robots capable of nuanced, adaptive interaction with unstructured environments.
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Read at arXiv cs.LG