Integrating Large Language Models into Robotic Autonomy
A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 88
Special Issue Editors
Interests: intelligent sensing; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: generative AI; large language models; educational AI; reinforcement learning; medical image analytics; AI in healthcare; human-AI interaction
Interests: machine learning; data mining; optimization; bioinformatics
Special Issue Information
Dear Colleagues,
Recent advancements in large language models (LLMs) are reshaping the landscape of artificial intelligence (AI) and unlocking new possibilities in robotic autonomy. It can be foreseen that LLMs will inspire the transformation of robotic autonomy. In robotics, domain-specific knowledge that includes sensing/perception, computation of kinematic/dynamic-based actions, and control theory is accumulated. LLMs bring a transformative capability to how robots understand, interact with, and navigate their environments, bridging symbolic reasoning with sensorimotor intelligence. LLMs can train robots to think and behave in a human style and coordinate and manage various operations intelligently. This Special Issue invites contributions that explore both theoretical frameworks and real-world applications of LLMs in robotics, especially in domains where voice, vision, and contextual learning converge to achieve complex autonomous behavior. Topics of interest include, but are not limited to, sensor-grounded perception learning, real-time decision-making through voice and text commands, robot-to-human/robot-to-robot interaction, and structured reasoning through multi-level representations.
This Special Issue spotlights exceptional research in integrating large language models into robotic autonomy, emphasizing the cutting-edge advances, innovations, developments, and emerging trends in locomotion, navigation, manipulation, representation, interpretation, and voice-based interaction. High-quality papers addressing both theoretical and practical applications of LLMs on robotic autonomy are welcome.
Dr. Qingquan Sun
Dr. Jennifer Jin
Dr. Xiangyu Li
Dr. Duy Ho
Guest Editors
Manuscript Submission Information
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Keywords
- LLMs in locomotion control
- LLMs in navigation and semantic planning
- LLMs in physical interaction and manipulation
- voice-based control and communication
- sensor-grounded perception
- learning, real-time decision-making through voice and text commands
- robot-to-human/robot-to-robot interaction
- structured reasoning through multi-level representations
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