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