[Submitted on 4 Jul 2026]
Title:HyperDCM: Dynamic Cluster Memory Replay in Hyperbolic Space for Continual Robotic Navigation Across Scenes
View a PDF of the paper titled HyperDCM: Dynamic Cluster Memory Replay in Hyperbolic Space for Continual Robotic Navigation Across Scenes, by Zhengfei Lu and 9 other authors
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Abstract:Continual learning in visual navigation remains challenging due to catastrophic forgetting and the difficulties associated with adapting to diverse and evolving environments. To address these issues, we propose Hyperbolic Dynamic Cluster Memory (HyperDCM), a structure-aware memory mechanism that enhances diffusion policy-based navigation through scene graph modeling and principled memory replay. HyperDCM extracts semantic scene triples from RGB observations using large vision-language models, encodes them into scene graph embeddings via a Relational Graph Convolutional Network (R-GCN), and projects the embeddings into hyperbolic space to enhance structural separability and retention in continual navigation. A dynamic clustering and structure-sensitive update strategy selects representative samples for memory replay, thereby preserving knowledge diversity and mitigating catastrophic forgetting. Experiments on multi-scene indoor and outdoor datasets demonstrate that HyperDCM achieves superior retention of past navigation capabilities and improved generalization compared to representative continual learning baselines adapted to diffusion policy navigation.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.16267 [cs.RO]
(or arXiv:2607.16267v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.16267
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xian Wei [view email] [v1] Sat, 4 Jul 2026 01:21:12 UTC (3,659 KB)
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