Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection
arXiv:2603.23800v2 Announce Type: replace-cross Abstract: We present a novel LLM-informed model-based planning framework, and a novel prompt selection method, for object search in partially-known environments. Our approach uses an LLM to estimate statistics about the likelihood of finding the target object when searching various locations throughout the scene that, combined with travel costs extracted from the environment map, are used to instantiate a model, thus using the LLM to inform planning and achieve effective search performance. Moreover, the abstraction upon which our approach relies
Ongoing advancements in LLM capabilities and robotics research are converging, making a framework for intelligent object search in complex environments a logical next step.
This development can significantly enhance the autonomy and efficiency of robotic systems in real-world applications, reducing the need for explicit programming in dynamic environments.
LLMs shift from purely generative tasks to actively informing and improving robotic planning and decision-making in previously unstructured problems.
- · AI/robotics research institutions
- · Logistics and warehousing sectors
- · Search and rescue operations
- · Home robotics manufacturers
- · Companies relying on manual object identification
- · Traditional robotics programming methods
- · Robotics firms slow to integrate AI planning
More sophisticated and flexible robotic object manipulation and navigation will become feasible.
Reduced operational costs and increased throughput in environments requiring complex object identification and retrieval will drive wider adoption of AI-enabled robots.
The integration of LLMs with sensory and motor systems in robotics could accelerate the development of truly general-purpose intelligent agents.
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