AINeutralarXiv – CS AI · 7h ago6/10
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AURA: Action-Gated Memory for Robot Policies at Constant VRAM
Researchers introduce AURA-Mem, a memory management system for robot policies that maintains constant memory footprint (4,224 bytes) regardless of episode length by using a learned gate to write only when observations would change actions. The approach reduces memory writes by 5-9x compared to KV-cache methods while matching performance on robotic tasks, addressing the bandwidth constraints of edge hardware used in embodied AI systems.