Fine-tuning World Action Models (WAMs): From multimodal data to reliable robot behavior

​Physical AI breakthroughs don't come from new models alone; they come from better data. As robotics teams race to build model-based policies for embodied AI systems, they face a new challenge: managing massive volumes of multimodal data, improving data quality, and creating feedback loops that continuously improve real-world performance. Join Skander Fourati, ML Solutions Engineer at Encord, and Anushrav Vatsa, AI Solutions Engineer at Weights & Biases, to learn how leading AI teams are building data-centric workflows for physical AI and fine-tune a World Action Model (WAM) for a new embodiment. You'll learn how Encord and Weights & Biases by CoreWeave work together to connect data curation, annotation, evaluation, experimentation, and model iteration into a unified development workflow that helps teams move faster from raw sensor data to reliable robot behavior.