Show HN: A curated list of academic papers and resources on Physical AI (github.com)

A new curated list of academic papers and resources focusing on Physical AI has been released, emphasizing Vision-Language-Action (VLA) models, world models, and embodied intelligence. This compilation represents a significant step for the AI/ML community as it highlights how AI systems can integrate perception, reasoning, and action in physical environments, pushing the boundaries of robotic capabilities. Key advancements noted include innovative models such as Helix, which merges semantic understanding from large language models with practical robot control, and the introduction of lightweight VLA models that achieve high performance with fewer parameters, making them more accessible for edge deployments. The list encompasses a wide array of topics such as action representation, lifelong learning, and safety mechanisms, illustrating the depth of research aimed at enhancing the interaction between AI and the physical world. Noteworthy contributions include the use of diffusion models for generating continuous action trajectories in robotic tasks and the establishment of ethical frameworks like the "Robot Constitution" to ensure safe deployment of these technologies. By systematically addressing the challenges and opportunities in Physical AI, this resource not only serves to guide ongoing research but also fosters collaborations that could lead to groundbreaking applications in robotics and automation.