[2605.10993] ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models
[Submitted on 9 May 2026]
Title:ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models
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Abstract:Memory capacity is a critical factor determining the performance of Vision-Language-Action (VLA) models in long-horizon manipulation tasks. Existing memory-augmented architectures primarily rely on linear or flat storage, lacking structural priors for manipulation categories and hierarchical organization. This deficiency hinders efficient experience retrieval and limits generalization to unseen long-horizon task compositions. Inspired by the hierarchical organization of human experience, we propose ECHO (Experience Consolidation and Hierarchical Organization), a novel memory framework operating within a Continuous Hierarchical Space. By employing a hyperbolic autoencoder, ECHO maps VLA hidden states into this space. Leveraging hyperbolic metrics and entailment constraint mechanisms, experience vectors are organized into a semantic memory tree that supports efficient top-down retrieval. In parallel, a background consolidation mechanism continuously refines the memory tree through geometric interpolation and structural splitting, supporting virtual memory synthesis in the continuous space. We integrate ECHO into the $\pi_0$ foundation model. Evaluations on LIBERO and preliminary real-world experiments demonstrate the effectiveness of our approach, notably achieving a 12.8% absolute improvement in execution success rate over the $\pi_0$ baseline on LIBERO-Long, while improving compositional generalization on cross-suite unseen long-horizon tasks.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.10993 [cs.RO]
(or arXiv:2605.10993v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.10993
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yanbin Hu [view email] [v1] Sat, 9 May 2026 13:06:33 UTC (16,577 KB)
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