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.
What to expect
A practical look at the data challenges slowing physical AI and novel architectures like WAMs
Real-world examples of multimodal data workflows spanning vision, language, action, and sensor streams
Best practices for connecting data quality, model evaluation, and experimentation into a continuous improvement loop
Insights into how Encord and Weights & Biases by CoreWeave help teams manage data and model development together
Live discussion and audience Q&A with experts working directly with robotics and physical AI teams
What you'll learn
Why data quality, not just model architecture, is becoming the primary bottleneck for physical AI development
How leading teams curate, annotate, version, and evaluate multimodal datasets for robotics and embodied AI
Techniques for identifying failure modes and connecting model performance back to underlying data issues
How to build a data flywheel that continuously improves WAMs and physical AI models through experimentation and evaluation
Ways to integrate Encord and Weights & Biases to create more reproducible, scalable physical AI workflows
Who should attend
AI and robotics researchers and scientists, mechanical and electrical engineers, AI engineers, software developers, and AI leaders.
Webinar
