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ARXIV:2604.18933 · ROBOTICS · SUBMITTED 22 APR · 02:13 UTC · FRESHNESS STALE
ARXIV:2604.18933ROBOTICSSUBMITTED 22 APR · 02:13 UTCFRESHNESS STALEYihuai Gao · Jinyun Liu · Shuang Li · Shuran Song · arXiv
A visuomotor policy that learns when and what to recall from memory for robotic manipulation, significantly improving performance on non-Markovian tasks.
Opportunity summary
Pain A visuomotor policy that learns when and what to recall from memory for robotic manipulation, significantly improving performance on non-Markovian tasks.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A visuomotor policy that learns when and what to recall from memory for robotic manipulation, significantly improving performance on non-Markovian tasks. Surprisingly, simply extending observation histories of a visuomotor policy often leads to a…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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…
Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A visuomotor policy that learns when and what to recall from memory for robotic manipulation, significantly improving performance on non-Markovian tasks.
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10.48550/arXiv.2604.18933A visuomotor policy that learns when and what to recall from memory for robotic manipulation, significantly improving performance on non-Markovian tasks.
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/.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
A visuomotor policy that learns when and what to recall from memory for robotic manipulation, significantly improving performance on non-Markovian tasks. Surprisingly, simply extending observation histories of a visuomotor policy often leads to a significant performance drop due...
METHOD
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 o...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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 RoboMim...
WHY NOW
Robotics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A visuomotor policy that learns when and what to recall from memory for robotic manipulation, significantly improving performance on non-Markovian tasks.
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Robotics
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7.0/10 public viability
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