physical ai

LiberAI, a physical AI startup developing foundation systems for general-purpose robots, is launching Liber-0, a World Action Model pretrained on more than 2.2 million hours of human experience to help robots learn how to interact with the physical world. (See video below.)

Liber-0 combines large-scale human video with directly recorded hand motions, visual and embodiment alignment, and embodiment-conditioned learning, with the aim of turning human experience into generalizable robot manipulation capabilities.

Founded in December 2025, LiberAI has designed its World Action Model to transfer human experience into generalizable robot manipulation capabilities. [Read more…] about From human experience to robot action: LiberAI launches Liber-0 world-action model

Most AI-powered robots effectively stop learning once they are deployed. Researchers at Skylark Labs, working with academics from Carnegie Mellon University and the University of California, Berkeley, have developed a new approach designed to enable robots to continue learning from successful experiences in the real world – without retraining their underlying AI models or forgetting what they already know.

The researchers call the new architecture Continual Field-Adaptive Models, or CFAMs, and describe the technology in a newly published research paper, Continual Field-Adaptive Models (CFAMs) for Post-Deployment Physical AI.

The central problem addressed by the research is becoming increasingly important as robots move from controlled laboratories and factories into less predictable environments. [Read more…] about Skylark Labs researchers develop AI system that enables robots to keep learning after deployment