Motivation and Scope x000D
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Physical AI systems comprise of four things: namely sensors like cameras and lidar, mechanical and electronic control unit, AI models to reason about the environment, and actuators to convert decisions to physical actions. It marries multiple domains like sensor design, perception, low-power real-time hardware design, and control loop action design. Autonomous driving is the most mature physical AI domain deployed for over 10 years, but it still has many open challenges. Humanoid robots are an emerging physical AI domain with potential for near term commercial deployment. One of the major challenges in physical AI is to scale to all real-world scenarios including corner cases in a safe manner. A scalable AI data flywheel is the most critical module to achieve this. Traditional physical AI models have a modular decomposition of perception and action tasks, but the community is increasingly moving towards a single end-to-end AI model. Furthermore, recent advancements in LLMs and VLMs are leading to VLA (Vision-Language-Action) based end-to-end models. In the future, there will likely be a convergence of physical AI models across different domains like driving and robotics. The proposed workshop covers the latest research and best practices in industrial research of physical AI by leaders in the domain. It also covers emerging technologies like VLA based foundation models, AI data flywheel, and cross-embodiment learning focused on Physical AI.