AURA-Mem uses an action-gated recurrent memory trained on closed-loop action error to deliver constant 4,224-byte state and 5-9x fewer writes than baselines while matching base policy success on LIBERO-Long.
Gated Memory Policy
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
Robotic manipulation tasks exhibit varying memory requirements, ranging from Markovian tasks that require no memory to non-Markovian tasks that depend on historical information spanning single or multiple interaction trials. Surprisingly, simply extending observation histories of a visuomotor policy often leads to a significant performance drop due to distribution shift and overfitting. To address these issues, we propose Gated Memory Policy (GMP), a visuomotor policy that learns both when to recall memory and what to recall. To learn when to recall memory, GMP employs a learned memory gate mechanism that selectively activates history context only when necessary, improving robustness and reactivity. To learn what to recall efficiently, GMP introduces a lightweight cross-attention module that constructs effective latent memory representations. To further enhance robustness, GMP injects diffusion noise into historical actions, mitigating sensitivity to noisy or inaccurate histories during both training and inference. On our proposed non-Markovian benchmark MemMimic, GMP achieves a 30.1% average success rate improvement over long-history baselines, while maintaining competitive performance on Markovian tasks in RoboMimic. All code, data and in-the-wild deployment instructions are available on our project website https://gated-memory-policy.github.io/.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Behavior Prompting Policy (BPP) is an in-context visuomotor policy that uses a single demonstration as a prompt to enable test-time adaptation on unseen drawing and tabletop tasks.
citing papers explorer
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Behavior Prompting Policy: Demonstrations as Prompts for Manipulation
Behavior Prompting Policy (BPP) is an in-context visuomotor policy that uses a single demonstration as a prompt to enable test-time adaptation on unseen drawing and tabletop tasks.