Physical AI in practice: Technical foundations that fuel human-machine interactions

This article explains physical AI's technical foundations and development lifecycle, using Diligent Robotics' hospital robot Moxi as a real-world example of human-machine collaboration.

• Physical AI systems understand, reason, learn, and interact with the physical world iteratively
• Development lifecycle includes data collection, model training, optimization, and edge deployment
• Training methodologies: reinforcement learning, physics-informed learning, imitation learning, simulation-based training
• Model optimization techniques: quantization and distillation reduce computational requirements for edge deployment
• Edge computing enables real-time autonomous decisions critical for safety-sensitive applications
• Moxi robot completed 1.2 million deliveries and saved 600,000 staff hours across hospital networks
• Business leaders must address cybersecurity, interoperability, safety, and ethical governance frameworks
• Risk-based governance approach balances regulatory compliance with innovation agility

Physical AI is transforming industries by enabling intelligent systems that collaborate with humans, anticipate needs, and operate autonomously in complex real-world environments.