Object search improved by commonsense and memory in changing environments
CMAR-search: Commonsense and Memory Augmented Reasoning for Object Search in Dynamic Interactive Environments
Robotics
Summary
Finding objects in large, busy spaces with many interactive places like drawers or cabinets can be very hard because existing methods don’t understand how these spaces are used. The authors created CMAR-search, which helps robots or systems by teaching them about functional areas and remembering where things usually are. This approach makes searching faster and smarter by focusing on the right spots and adapting when items are moved around. Tests showed that this method works better than previous ones in both simulations and real-world scenarios.
What this means in practice
- •For robotics engineers: Develop robots that efficiently find objects in homes or offices with many interactive areas and changing layouts.
- •For home automation developers: Create intelligent assistants that adaptively search and locate items moved around dynamic living spaces.$Commercial implications: Enables commercial smart home robots to offer improved object search capabilities, enhancing user convenience.
Authors
Kaiyao Liao, Qingfeng Li, Xinlei Zhang, Chen Chen, Qing Sun, Jianwei Niu
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.