AURA: Action-Gated Memory for Robot Policies at Constant VRAM
The paper presents AURA-Mem, a novel memory architecture designed for robotic policies that operates with constant VRAM. This approach utilizes an action-gated mechanism to optimize memory writes, significantly reducing the number of writes needed compared to traditional methods. The results indicate that AURA-Mem maintains accuracy while using fewer resources, making it suitable for bandwidth-limited edge hardware.
- ▪AURA-Mem targets the limitations of memory in robots, contrasting with datacenter memory systems.
- ▪The architecture uses a learned gate that writes to memory only when necessary, reducing unnecessary writes.
- ▪In benchmarks, AURA-Mem achieves comparable accuracy to existing methods while using significantly fewer memory writes.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2606.02775 |
| Publication time | Wed, 03 Jun 2026 00:00:00 -0400 |
| Retrieval time | 2026-06-03T04:11:55.408Z |
| Last seen | 2026-06-03T04:11:55.408Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Artificial Intelligence arXiv:2606.02775 (cs) [Submitted on 1 Jun 2026] Title:AURA: Action-Gated Memory for Robot Policies at Constant VRAM Authors:Josef Chen View a PDF of the paper titled AURA: Action-Gated Memory for Robot Policies at Constant VRAM, by Josef Chen View PDF HTML (experimental) Abstract:The KV-cache is the right memory for datacenters but the wrong memory for robots. Datacenter inference batches many short requests and resets them, amortizing an attention cache across a crowd. Embodied agents instead run one long, non-resetting episode on bandwidth-limited edge hardware, where high-bandwidth memory and flash are scarce, flash has finite write endurance, and memory writes rather than compute can become the binding constraint.
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