Remember Smarter: Visual History Compressor and Hyperbolic Experience Space for Robotic Memory

• URL: http://arxiv.org/abs/2608.15269v1
• Date: Sat, 15 Aug 2026 15:13:33 GMT
• Title: Remember Smarter: Visual History Compressor and Hyperbolic Experience Space for Robotic Memory
• Abstract summary: Remember Smarter (RS) is a plug-and-play module with complementary visual-history and hyperbolic experience-memory branches.<n>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.
• Score: 54.400138177707504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Dual Latent Memory in Vision-Language-Action Models for Robotic Manipulation [96.18832998704329]
  We introduce LaMem-VLA, a latent-memory-native framework that reconstructs historical experience into latent memory tokens.<n>LaMem-VLA enables memory to directly participate in VLA reasoning and guide action generation under a bounded context.
  arXiv  Detail & Related papers  (2026-07-08T16:26:06Z)
• Semantic-Aware Adaptive Visual Memory for Streaming Video Understanding [55.7992006853979]
  SAVEMem is a framework that brings semantic awareness into memory generation and lets the retrieval scope adapt per query.<n>We present SAVEMem, a training-free dual-stage framework that brings semantic awareness into memory generation and lets the retrieval scope adapt per query.
  arXiv  Detail & Related papers  (2026-05-08T15:40:40Z)
• VPWEM: Non-Markovian Visuomotor Policy with Working and Episodic Memory [31.464584758455356]
  VPWEM is a non-Markovian visuomotor policy equipped with working and episodic memories.<n>It exploits both short-term and episode-wide information for action generation with nearly constant memory and computation per step.
  arXiv  Detail & Related papers  (2026-03-05T07:52:50Z)
• RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies [54.23445842621374]
  Memory is critical for long-horizon and history-dependent robotic manipulation.<n>Recent vision-language-action (VLA) models have begun to incorporate memory mechanisms.<n>We introduce RoboMME: a large-scale standardized benchmark for evaluating and advancing VLA models.
  arXiv  Detail & Related papers  (2026-03-04T21:59:32Z)
• ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL [48.214881182054164]
  We propose ELMUR, a transformer architecture with structured external memory.<n>ELMUR extends effective horizons up to 100,000 times beyond the attention window.<n>It achieves a 100% success rate on a synthetic T-Maze task with corridors up to one million steps.
  arXiv  Detail & Related papers  (2025-10-08T15:50:34Z)
• MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation [59.31354761628506]
  Temporal context is essential for robotic manipulation because such tasks are inherently non-Markovian, yet mainstream VLA models typically overlook it.<n>We propose MemoryVLA, a Cognition-Memory-Action framework for long-horizon robotic manipulation.<n>We evaluate it on 150+ simulation and real-world tasks across three robots.
  arXiv  Detail & Related papers  (2025-08-26T17:57:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.