MessyMem: Learning-from-Doing Memory for Mobile Manipulation

Abstract:Mobile manipulators deployed across many rooms and visits should improve with experience: after discovering that a cabinet is locked or finding an object in a drawer, the robot should reuse that knowledge rather than start each task from scratch. Yet today's robots often treat each task as new: compact scene representations omit interaction-derived knowledge, raw video histories are difficult to query, and VLM planners reason at inference time without persistently updating what the robot knows. We present MessyMem, a persistent memory system that enables mobile manipulators to learn from experience and reuse that knowledge across future tasks. It maintains a spatially grounded 3D scene graph of objects and locations, augments it with properties and outcomes learned through interaction, and links visual observations for fine-grained recall. We evaluate MessyMem in simulation and on a real mobile manipulator. In a continuous 25-task simulation spanning over 3 hours, MessyMem achieves 80.0% task progress, outperforming the strongest ablation by 14.8 percentage points and the strongest external baseline by 28.9 points, while retrieving task-relevant evidence from thousands of stored keyframes and over an hour into the past.

Accepted at CoRL 2026. 26 pages, 8 figures. Project page: this https URL

Robotics (cs.RO)

arXiv:2609.15976 [cs.RO]

(or arXiv:2609.15976v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.15976 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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