Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies
It is widely appreciated that robotic policies require long-term memory to perform complex tasks. However, directly conditioning policies on all past observations leaves policy learning susceptible to learning spurious shortcuts, and introduces large latency at policy deployment. As such, today's memory-augmented policies curate what to remember, either by retaining only recent frames, selected keyframes, or textual summaries, or by learning latent representations of the visual stream alone. In this work, we propose an alternative approach IMPRINT, a policy-agnostic latent memory framework that augments vision-language-action (VLA) models with multi-stream memory without explicitly constr...
| Authors | Yilin Wu, Rutav Shah, Shuaiyi Huang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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