Xinghua Jia

Beyond Vision-Language-Action, the next generation of Physical AI requires a fundamentally different understanding of intelligence. While recent progress in VLA models has significantly improved high-level semantic reasoning and robotic policy learning, physical interaction with the real world remains constrained by limited perception modalities and simplified assumptions about embodiment. This talk revisits Embodied AI from first principles. Inspired by biological cognition and human sensorimotor systems, we discuss why future Physical AI must evolve beyond vision and language toward rich multimodal perception—including force, tactile sensing, proprioception, contact dynamics, and continuous interaction with the physical world. We further propose a layered framework for Physical AI, covering perception, cognition, world modeling, robot foundation models, action generation, data engines, simulation, and lifelong learning. Rather than treating these components independently, we argue that they should form a closed-loop embodied intelligence system capable of continuous adaptation through interaction. Finally, we discuss pragmatic engineering principles for deploying Physical AI in today’s industrial environments, outlining an incremental roadmap from narrowly specialized robotic applications toward more general-purpose embodied intelligence.