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Large Language Models and 3D Vision for Intelligent Robotic Perception and Autonomy.

Vinit Mehta¹, Charu Sharma¹, Karthick Thiyagarajan²

¹Machine Learning Lab, International Institute of Information Technology (IIIT), Hyderabad 500032, Telangana, India.

Sensors (Basel, Switzerland)

|October 29, 2025

Summary

Large Language Models (LLMs) combined with 3D vision are revolutionizing robotic sensing. This integration enhances robots

Keywords:

3D vision embodied agents human robot interaction large language models robot sensing scene understanding sensor applications visual sensing

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Area of Science:

Robotics and Artificial Intelligence
Computer Vision
Natural Language Processing

Background:

Rapid advancements in AI and robotics necessitate sophisticated sensing capabilities.
Integrating Large Language Models (LLMs) with 3D vision offers a novel approach to robotic perception.
Bridging linguistic intelligence and spatial understanding is key for advanced human-robot interaction.

Purpose of the Study:

To provide a comprehensive review of LLMs and 3D vision integration for robotic sensing.
To analyze state-of-the-art methodologies, applications, and challenges in this interdisciplinary field.
To identify future research directions for more intelligent and autonomous robotic systems.

Main Methods:

Introduction to foundational principles of LLMs and 3D data representations.
In-depth examination of 3D sensing technologies relevant to robotics.
Exploration of advancements in scene understanding, text-to-3D generation, object grounding, and embodied agents.

Main Results:

Highlighting cutting-edge techniques like zero-shot 3D segmentation and language-guided manipulation.
Discussion of multimodal LLMs integrating diverse sensory inputs (touch, auditory, thermal) for enhanced comprehension.
Cataloging benchmark datasets and evaluation metrics for 3D-language and vision tasks.

Conclusions:

The convergence of LLMs and 3D vision is crucial for next-generation robotic sensing.
Key challenges include adaptive architectures, cross-modal alignment, and real-time processing.
Future research will enable more context-aware and autonomous robotic sensing systems.