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This research introduces "Remember Smarter," a novel module enhancing robot policies by compressing visual history via Mamba architectures and storing experience in hyperbolic space to improve long-horizon task success rates without expanding context windows. The approach demonstrates significant performance gains on the LIBERO-Plus benchmark, offering a practical solution for scaling robotic memory retention while maintaining efficient inference speeds.
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Remember Smarter: Visual History Compressor and Hyperbolic Experience Space for Robotic Memory

arXiv2 min read

arXiv:2608.15269v1 Announce Type: new Abstract: Long-horizon robot policies require compact access to recent observations and reusable experience without expanding the vision-language-action (VLA) context. We introduce Remember Smarter (RS), a plug-and-play module with complementary visual-history and hyperbolic experience-memory branches. Its visual branch compresses multi-view patch histories using bidirectional spatial Mamba and causal temporal Mamba, then exposes the resulting memory to action-facing hidden states through residual cross-attention while leaving the VLM visual-token stream unchanged. Its experience branch stores successful final-layer VLM states in a Poincare VAE space, organizes them hierarchically, and asynchronously converts retrieved experience into geodesic prompt tokens without blocking action inference. When adapted to pi0, RS increases total success on LIBERO-Plus from 53.6% to 70.6% and achieves substantial performance gains in real-robot experiments designed to evaluate memory retention and experience utilization.

Abstract:Long-horizon robot policies require compact access to recent observations and reusable experience without expanding the vision-language-action (VLA) context. We introduce Remember Smarter (RS), a plug-and-play module with complementary visual-history and hyperbolic experience-memory branches. Its visual branch compresses multi-view patch histories using bidirectional spatial Mamba and causal temporal Mamba, then exposes the resulting memory to action-facing hidden states through residual cross-attention while leaving the VLM visual-token stream unchanged. Its experience branch stores successful final-layer VLM states in a Poincare VAE space, organizes them hierarchically, and asynchronously converts retrieved experience into geodesic prompt tokens without blocking action inference. When adapted to pi0, RS increases total success on LIBERO-Plus from 53.6% to 70.6% and achieves substantial performance gains in real-robot experiments designed to evaluate memory retention and experience utilization.

Robotics (cs.RO)

arXiv:2608.15269 [cs.RO]

(or arXiv:2608.15269v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.15269 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 219 words

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