aiqbench analysis
This paper applies commonsense reasoning and accumulated memory to robotic object search, building a scene graph that groups areas by function (storage, leisure) and plans search hierarchically across areas and containers. The reported gains in success rate and efficiency are claimed against baselines, and the abstract gives no details on the commonsense source or how it is encoded, so the claims need checking against the full paper's experiments. For AI readers, the notable idea is using functional priors and memory to make embodied planning adapt to object relocations.
CMAR-search: Commonsense and Memory Augmented Reasoning for Object Search in Dynamic Interactive Environments
arXiv:2610.30606v1 Announce Type: new Abstract: Dynamic interactive object search in large-scale human environments presents substantial challenges for existing methods. Current scene representations like 3D Scene Graphs (3DSG) only provide coarse-grained spatial segmentation and cannot identify functional areas such as storage or leisure areas. Without functional area understanding, existing methods are constrained to exhaustive sequential exploration at large scales, resulting in inefficient search behaviors -- particularly in open-layout environments with numerous interactive objects such as drawers and cabinets. Moreover, these methods lack adaptability to environmental dynamics such as object relocations. To address these limitations, this paper proposes CMAR-search, a novel framework built upon Commonsense and Memory Augmented Reasoning (CMAR). Our approach leverages commonsense about area functionalities and aggregates environmental memory to construct a Functional 3D Scene Graph (F3DSG), which organizes the environment into functional areas with their associated containers. Through this structured representation, CMAR enables hierarchical action planning at both macro-area and micro-container levels, empowering the system to efficiently identify and inspect semantically relevant areas for effective object search. Notably, CMAR continuously integrates real-time perception, accumulated memory, and commonsense to dynamically relocalize objects in changing environments. Extensive experiments in simulation and real-world settings demonstrate that CMAR-search significantly surpasses state-of-the-art baselines in both success rate and search efficiency for object search in dynamic interactive environments.
Abstract:Dynamic interactive object search in large-scale human environments presents substantial challenges for existing methods. Current scene representations like 3D Scene Graphs (3DSG) only provide coarse-grained spatial segmentation and cannot identify functional areas such as storage or leisure areas. Without functional area understanding, existing methods are constrained to exhaustive sequential exploration at large scales, resulting in inefficient search behaviors -- particularly in open-layout environments with numerous interactive objects such as drawers and cabinets. Moreover, these methods lack adaptability to environmental dynamics such as object relocations. To address these limitations, this paper proposes CMAR-search, a novel framework built upon Commonsense and Memory Augmented Reasoning (CMAR). Our approach leverages commonsense about area functionalities and aggregates environmental memory to construct a Functional 3D Scene Graph (F3DSG), which organizes the environment into functional areas with their associated containers. Through this structured representation, CMAR enables hierarchical action planning at both macro-area and micro-container levels, empowering the system to efficiently identify and inspect semantically relevant areas for effective object search. Notably, CMAR continuously integrates real-time perception, accumulated memory, and commonsense to dynamically relocalize objects in changing environments. Extensive experiments in simulation and real-world settings demonstrate that CMAR-search significantly surpasses state-of-the-art baselines in both success rate and search efficiency for object search in dynamic interactive environments.
Published in ICRA 2026. 8 pages, 5 figures
Robotics (cs.RO)
arXiv:2610.30606 [cs.RO]
(or arXiv:2610.30606v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.30606 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
2026 IEEE International Conference on Robotics and Automation (ICRA), pp. 18586-18593, 2026
https://doi.org/10.1109/ICRA57385.2026.11697035 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 331 words
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