The rapid advancements in robotics and artificial intelligence (AI) over the past decade have unlocked new possibilities for autonomous systems across a wide range of applications. However, deploying robots in complex, dynamic, and unstructured environments remains a significant challenge. Such scenarios require robots to exhibit not only physical robustness but also advanced cognitive capabilities to perceive, reason, and act autonomously under uncertainty. Generative AI, with its ability to create realistic simulations, learn from limited data, and predict complex interactions, provides transformative potential to address these challenges. By integrating generative AI with intelligent control, researchers can develop innovative solutions to enable robots to operate effectively in demanding environments like disaster response, remote exploration, healthcare, and industrial automation. This fusion of AI and control frameworks offers a holistic approach to modeling closed-loop systems, encompassing sensing, perception, planning, and control, essential for robust and adaptable autonomous operations in real-world scenarios.
This Research Topic focuses on the intersection of generative AI, intelligent control and robotics, aiming to explore how combinations of AI and control can empower robots to operate effectively in challenging environments. By leveraging the unique capabilities of generative AI—such as creating realistic simulations, learning from sparse data, and predicting complex interactions—researchers and practitioners can develop innovative control solutions for real-world deployment in domains such as disaster response, remote exploration, healthcare, and industrial automation.
The Research Topic invites high-quality contributions from researchers, engineers, and industry professionals to share cutting-edge advancements, methodologies, and applications. In particular, it seeks to address key barriers to deploying AI and intelligent control-powered robots in challenging environments, such as robustness, adaptability, and generalization. Unlike traditional AI approaches, generative AI offers a more holistic framework for modeling the closed-loop systems of sensing, perception, planning and control that are critical for autonomous operation in the real world.
We welcome high-quality original research articles, case studies, and reviews that explore the integration of generative AI and intelligent control in robotics. Topics of interest include, but are not limited to: • Generative AI for robotic perception, planning, and control. • Simulation and digital twins powered by generative AI for training and validation. • Learning and intelligent control for real-world challenges. • Generative models with physical constraints for robotics. • Data-efficient generative AI for low-resource environments. • Foundation models for robotics and their practical applications. • Case studies of generative AI in challenging scenarios, such as disaster recovery, industrial automation, or medical robotics. • Surveys analyzing challenges, opportunities, and future directions for generative AI in robotics, with a focus on overcoming deployment barriers in complex environments.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Data Report
Editorial
FAIR² Data
FAIR² DATA Direct Submission
General Commentary
Hypothesis and Theory
Methods
Mini Review
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Data Report
Editorial
FAIR² Data
FAIR² DATA Direct Submission
General Commentary
Hypothesis and Theory
Methods
Mini Review
Opinion
Original Research
Perspective
Policy and Practice Reviews
Review
Systematic Review
Technology and Code
Keywords: Generative AI, Intelligent control, Autonomous systems, Foundation models, Adaptability and robustness
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.
