The research scope of the journal focuses on the theoretical foundations, core technologies, system development, industrial applications, and ethical norms of artificial intelligence (AI) and robotics. It also emphasizes interdisciplinary integration and cutting-edge exploration, with specific classifications as follows:

1.Core Theories and Technologies of Artificial Intelligence

Basic Algorithm Research: Covers the improvement and innovation of algorithms such as machine learning, deep learning, and reinforcement learning, as well as the optimization of basic models including neural networks and knowledge discovery. It also includes theoretical research on automated reasoning and explainable AI to address the transparency and credibility of AI models.

Natural Language and Vision Technologies: Involves natural language processing technologies like speech recognition and synthesis, machine translation, and natural language understanding. It also encompasses computer vision-related research such as image processing, 2D/3D visual understanding, and visual odometry, providing technical support for robots to perceive the external world.

2.Core Technologies and System Development of Robotics

Robot Perception and Control: Focuses on perception technologies for robots, such as sensor fusion, tactile perception, and scene graph reasoning. It also includes control technologies like motion planning, optimal predictive control, and whole-body control to solve the problem of flexible operation of robots in dynamic environments.

Special Robot Systems: Includes the design and development of special types of robots such as flexible robots, bionic robots, swarm robots, and micro-robots. It also involves the cooperative operation mechanism of multi-agent systems to adapt to operational needs in different scenarios.

Robot Hardware and Simulation: Covers hardware-related research such as robot mechanical structure design and drive device development. It also includes simulation technologies like system simulation and industrial process modeling, providing a testing and optimization platform for robot research and development.

3.Human-Robot Interaction and Collaboration

This field focuses on the collaborative models between humans and robots, such as shared autonomous control, intent inference, and tactile interaction technologies to improve the fluency of human-robot collaboration. It also includes the application of trustworthy AI in human-robot interaction, as well as research on user trust evaluation and interaction security assurance, promoting the safe integration of robots into human life and work scenarios.

4.Multi-Domain Application Research

Industry and Manufacturing: Involves the application of industrial robots in automated production, AI-driven production process optimization, and logistics robot scheduling, supporting the development of Industry 4.0.

Medical Care and People's Livelihood: Covers the development of surgical robots and rehabilitation assistive robots, as well as the application of AI-assisted diagnosis systems and patient monitoring robots. It also includes research on intelligent teaching robots and AI-assisted learning systems in the education field.

Transportation and Urban Areas: Focuses on the development of autonomous vehicles and drone delivery systems. It also includes the application of AI and robotics in smart city-related scenarios such as intelligent traffic scheduling, urban infrastructure maintenance, and energy optimization.

Other Special Scenarios: Includes intelligent plant protection robots in agriculture, AI risk control robots in the financial field, as well as application research of robots in extreme environments such as space robots and field operation robots.

5.Ethics, Policies, and Interdisciplinary Expansion

Ethics and Norms: Explores ethical issues such as privacy protection, fairness, and responsibility division brought by AI and robotics technologies. It studies relevant industry norms and regulatory policies to promote the responsible development of technologies.

Interdisciplinary Integration: Covers interdisciplinary research such as the application of AI and cognitive science, and quantum computing in AI. It also includes the cross-disciplinary application of AI and robotics technologies in fields such as environmental monitoring and climate change response, expanding the application boundaries of technologies.