ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models

• URL: http://arxiv.org/abs/2605.10993v1
• Date: Sat, 09 May 2026 13:06:33 GMT
• Title: ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models
• Abstract summary: Memory capacity is a critical factor determining the performance of Vision-Language-Action (VLA) models in long-horizon manipulation tasks.<n>We propose ECHO, a novel memory framework operating within a Continuous Hierarchical Space.<n>By employing a hyperbolic autoencoder, ECHO maps VLA hidden states into this space.
• Score: 40.14860028616163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 $π_0$ 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 $π_0$ baseline on LIBERO-Long, while improving compositional generalization on cross-suite unseen long-horizon tasks.

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