# Language-Enhanced Mobile Manipulation for Efficient Object Search in Indoor Environments

## TL;DR

This paper presents GODHS, a language-enhanced hierarchical navigation framework that integrates semantic perception and spatial reasoning, enabling robots to search for objects more efficiently in indoor environments.

## Contribution

It introduces a novel framework combining large language models with hierarchical decision-making and a heuristic motion planner for improved object search.

## Key findings

- GODHS outperforms traditional search strategies in efficiency.
- Semantic reasoning improves search accuracy.
- Framework validated in Isaac Sim environment.

## Abstract

Enabling robots to efficiently search for and identify objects in complex, unstructured environments is critical for diverse applications ranging from household assistance to industrial automation. However, traditional scene representations typically capture only static semantics and lack interpretable contextual reasoning, limiting their ability to guide object search in completely unfamiliar settings. To address this challenge, we propose a language-enhanced hierarchical navigation framework that tightly integrates semantic perception and spatial reasoning. Our method, Goal-Oriented Dynamically Heuristic-Guided Hierarchical Search (GODHS), leverages large language models (LLMs) to infer scene semantics and guide the search process through a multi-level decision hierarchy. Reliability in reasoning is achieved through the use of structured prompts and logical constraints applied at each stage of the hierarchy. For the specific challenges of mobile manipulation, we introduce a heuristic-based motion planner that combines polar angle sorting with distance prioritization to efficiently generate exploration paths. Comprehensive evaluations in Isaac Sim demonstrate the feasibility of our framework, showing that GODHS can locate target objects with higher search efficiency compared to conventional, non-semantic search strategies. Website and Video are available at: https://drapandiger.github.io/GODHS