📚︎Track 1: Foundations of AI and Physical Intelligence

• Artificial Intelligence Theory

• Physical Intelligence Theory

• Embodied Cognition Theory

• Intelligent Computing Models

• Physical World Modeling

• Multimodal Intelligence Theory

• Learning and Reasoning Methods

• Physical Commonsense Representation

• Intelligent System Evaluation

📚︎Track 2: Embodied Intelligence and Robotics

• Embodied Intelligence

• Robot Learning

• Robot Perception

• Robot Control

• Human-Robot Collaboration

• Autonomous Navigation

• Manipulation and Grasping

• Multi-Robot Systems

• Bio-Inspired Robotics

📚︎Track 3: Physics-Informed AI and Modeling

• Physics-Informed Machine Learning

• Physics-Constrained Neural Networks

• Differentiable Simulation

• Physical Model Learning

• Scientific Machine Learning

• Hybrid Modeling Methods

• Dynamics Modeling

• Physics Knowledge Embedding

• Simulation-to-Reality Transfer

📚︎Track 4: Perception, Control and Physical Interaction

• Multimodal Perception

• Visual Perception

• Tactile Perception

• Force Feedback

• Motion Planning

• Adaptive Control

• Reinforcement Learning Control

• Physical Interaction Learning

• Dexterous Manipulation

📚︎Track 5: Physical Intelligent Systems and Computing

• Edge Intelligence Systems

• Real-Time Intelligent Computing

• Embedded Artificial Intelligence

• Neuromorphic Computing

• Brain-Inspired Computing

• Physical Computing Architectures

• Intelligent Sensor Systems

• Distributed Intelligent Systems

• Computing Resource Optimization

📚︎Track 6: Applications, Ethics and Emerging Challenges

• Intelligent Manufacturing

• Autonomous Driving

• Intelligent Medical Robotics

• Intelligent Transportation Systems

• Smart Agriculture

• Space and Deep-Sea Exploration

• Physical Intelligence Security

• Trustworthy Artificial Intelligence

• Future Physical Intelligence Challenges