The integration of Large Language Models (LLMs) into the computational intelligence of robotic systems represents a significant combined advancement in the field of artificial intelligence and robotics. LLMs, such as GPT-4, are capable of understanding and generating human-like text, offering new possibilities for enhancing the cognitive capabilities of robots. By leveraging LLMs, robots can achieve more sophisticated natural language processing, enabling better human-robot interaction, improved decision-making, and more adaptive problem-solving skills. This fusion of technologies makes it possible for robots to understand and respond to complex commands, learn from vast amounts of unstructured data, and operate autonomously in dynamic environments. The potential applications span across various fields, including healthcare, domestic use, manufacturing, and service industries where intelligent robots can significantly improve efficiency and performance.
This Research Topic aims to explore the transformative potential of integrating Large Language Models (LLMs) with computational intelligence to enhance the capabilities and performance of current robotic systems. We seek to highlight cutting-edge research and innovative applications that demonstrate how the synergy between LLMs and computational intelligence can lead to breakthroughs in areas such as robot manipulation, human-robot interaction, autonomous decision-making, natural language understanding, and real-time problem-solving. By bringing together experts from diverse fields, including artificial intelligence, robotics, machine learning, and natural language processing, this collection aspires to provide a comprehensive overview of the current state-of-the-art, identify emerging trends, and inspire future research directions. The ultimate goal is to foster a deeper understanding of how these technologies can collaboratively advance the field of robotics, making intelligent robotic systems more adaptable, intuitive, and resilient in performing complex tasks in dynamic environments.
We invite researchers and practitioners to contribute original research, review articles, case studies, and technical notes that address the following themes :
● Human-Robot Interaction
● Large Language Models (LLMs)
● Computational Intelligence
● Natural Language Processing (NLP)
● Human-Robot Collaboration
● Robot Grasping
● Robot Manipulation
● Robot Learning
● Robot Control
● Robot Companions
● Robot Safety
● Robot Planning
● Social Human-Robot Interaction
● Dual Arm Manipulation
● Collision Avoidance
● Autonomous Systems
Authors are encouraged to submit manuscripts that provide new insights, theoretical advancements, or practical implementations that push the boundaries of how LLMs can synergize with computational intelligence to create more advanced, intuitive, and capable robotic systems.
Keywords: LLM, Large Language Model, GPT, GPT4, ChatGPT, Computational Intelligence, Natural Language Processing, Robotics
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
