Topic: From Brain-Inspired Al to Embodied Intelligence: Efficient Learning and Neuromorphic Approaches for Next-Generation Robots

The rapid advancement of artificial intelligence is reshaping the development of intelligent robots, while growing demands for real-time perception, adaptive learning, energy efficiency, and autonomous decision-making are driving the exploration of new computational paradigms beyond conventional deep learning. Inspired by the remarkable efficiency, adaptability, and event-driven information processing mechanisms of biological brains, brain-inspired artificial intelligence and neuromorphic intelligence have emerged as promising approaches for developing more efficient and adaptive robotic systems.

This Special Issue aims to explore the emerging intersection of brain-inspired AI, efficient learning, neuromorphic intelligence, and embodied intelligence, with a particular focus on their applications in next-generation robots. We welcome innovative research on spiking neural networks, neuromorphic computing, brain-inspired learning and optimization, efficient and continual learning, event-driven perception, multimodal sensory processing, robot learning, adaptive decision-making, intelligent control, and embodied intelligence. Contributions addressing the integration of these technologies into real-world robotic systems, including autonomous robots, mobile robots, humanoid robots, and human–robot interaction, are particularly encouraged.

The Special Issue seeks to bridge neural computation and embodied robotic intelligence, fostering the development of robots that can perceive, learn, reason, and interact with their environments in a more efficient, adaptive, and intelligent manner. Both theoretical advances and experimental studies demonstrating novel algorithms, architectures, hardware–software co-design, and robotic applications are welcome.

Topics of Interest include, but are not limited to:

● Brain-Inspired Neural Computation and Learning;

● Spiking Neural Networks and Neuromorphic Intelligence;

● Neuromorphic Computing and Hardware–Software Co-design;

● Efficient, Continual, and Adaptive Learning;

● Event-Driven and Multimodal Robot Perception;

● Robot Learning, Decision-Making, and Adaptive Control;

● Brain-Inspired and Embodied Intelligence;

● Neuromorphic and Brain-Inspired Robotic Systems.