The integration of neuroscience with robotics has seen substantial growth in recent years, with breakthroughs in understanding the brain's neural networks and how these principles can inspire autonomous systems. Neuromorphic engineering—the design of circuits and systems that mimic the brain's structure and functioning—has been a key approach in developing autonomous robots capable of learning, self-organizing, and making decisions in real-time environments. These developments aim to create robots that can adapt to their surroundings, learn new tasks autonomously, and interact intelligently with humans and other agents in a manner similar to biological organisms.

However, challenges remain in scaling up these systems and ensuring they function robustly in dynamic, real-world environments. This research topic seeks to explore how neuromorphic engineering and brain-inspired algorithms can be applied to enhance the design, control, and deployment of autonomous robots and other embodied systems. We aim to gather cutting-edge research that bridges the gap between neuroscience, artificial intelligence, and robotic engineering to push the boundaries of autonomous, self-learning systems.

This research topic focuses on the latest advancements in neuromorphic engineering and brain-inspired control for autonomous systems. We invite research that addresses both the theoretical and practical aspects of developing and deploying embodied autonomous systems that can operate intelligently in diverse environments. Contributions are encouraged that examine brain-inspired algorithms, computational models of biological neural networks, and experimental work in embodied robotics, with a particular emphasis on real-world applications in areas such as healthcare, human-robot interaction, and autonomous vehicles.

Our Topics of Interest are:

1、Neuromorphic Control Systems for Autonomous Robots:

1）Development of neuromorphic control systems based on brain-inspired algorithms (e.g., spiking neural networks, reinforcement learning).
2）Exploration of how these control systems can be integrated with robotic hardware for real-time decision-making and adaptive behavior.
3）Studies on how robots can autonomously adjust to environmental changes using neuromorphic principles.

2、Brain-Inspired Algorithms for Autonomous Learning:

1）Investigating algorithms that mimic biological learning and cognition, such as Hebbian learning, synaptic plasticity, and neural network training mechanisms.
2）Research on autonomous learning in robots through unsupervised, self-supervised, and reinforcement learning paradigms, inspired by the brain’s ability to learn and adapt to new tasks.
3）Application of these algorithms in real-time systems, focusing on the optimization of robotic performance and decision-making.

3、Neuromorphic Robotics for Human-Robot Interaction (HRI):

1）Development of neuromorphic robots capable of interacting naturally and effectively with humans in complex environments.
2）Studies on how brain-inspired algorithms can be used to improve robot empathy, communication, and collaboration with humans.
3）Investigations into the role of emotion, intention, and cognition in human-robot interaction.

4、Embodied Autonomous Systems in Real-World Applications:

1）Case studies on the deployment of embodied autonomous robots in fields such as healthcare, manufacturing, agriculture, and urban infrastructure.
2）Research on the integration of neuromorphic control systems in prosthetics, wearable devices, and assistive technologies.
3）How embodied robots can function autonomously in complex, dynamic, and unstructured environments (e.g., smart homes, search and rescue missions).

5、Large-Scale Neural Simulations for Robotic Control:

1）Exploration of large-scale simulations of biological neural microcircuits and how they can inform the development of advanced robotic systems.
2）Application of these models to understand neural plasticity, self-organization, and complex behaviors in embodied systems.
3）Techniques for simulating and testing robotic systems using brain-inspired models before deployment in real-world scenarios.

6、Neuroscience-Driven Engineering for Robotic Devices:

1）Integration of neuroscience principles into the design of robotic hardware and mechatronics.
2）Studies on the role of bio-inspired sensors, actuators, and feedback loops in enabling autonomous robotics.
3）Exploration of how neural principles can guide the engineering of devices that interact with the human body (e.g., neuroprosthetics, brain-computer interfaces).

7、Ethical and Societal Impact of Neuromorphic Autonomous Systems:

1）Ethical considerations in the deployment of autonomous robots and AI systems in society, particularly in sensitive areas such as healthcare and personal assistance.
2）Social implications of brain-inspired robots in everyday life, including the role of trust, privacy, and autonomy.
3）Regulation and policy recommendations for the responsible development and use of neuromorphic autonomous systems.

Are you interested in contributing a manuscript to this Research Topic?

Neuromorphic Engineering and Brain-Inspired Control for Autonomous Robotics: Bridging Neuroscience and AI for Real-World Applications