Debugging Physical AI Models at Scale with Multimodal Data - August 11, 2026

Name: Debugging Physical AI Models at Scale with Multimodal Data - August 11, 2026
Start: 2026-08-11
End: 2026-08-11

Aug 11, 2026

9:00 AM - 10:00 AM PST

Online. Register for the Zoom!

About this event

As robotics and autonomous vehicle teams move from traditional perception models to end-to-end Physical AI systems, understanding model behavior is becoming harder than ever. These models ingest synchronized inputs from cameras, sensors, and other data streams, but their decisions can be difficult to explain, reproduce, and improve.

Join Voxel51 for a live workshop on how multimodal data workflows in FiftyOne help teams inspect, search, and debug complex Physical AI datasets and explain black-box model behavior at scale. We’ll show how teams can work with synchronized video and sensor data, query for similar scenarios across their datasets, and uncover patterns behind model failures faster than playback-only visualization tools allow.

You’ll learn how to use multimodal data to investigate questions like: when did the model swerve, miss an object, misinterpret a scene, or behave unexpectedly — and how can you find every similar moment across your dataset?

Designed for robotics, AV, and machine learning teams, this session will show how FiftyOne helps turn multimodal data into a scalable workflow for model evaluation, debugging, and improvement. View more Computer Vision events here.

Host

What you’ll learn:

How multimodal data workflows in FiftyOne help inspect, search, and debug complex Physical AI datasets
How to work with synchronized video and sensor data to understand model behavior
How to query for similar failure scenarios across large datasets
How to uncover patterns behind model failures faster than playback-only visualization tools
How to investigate questions like: when did the model swerve, miss an object, misinterpret a scene, or behave unexpectedly
How to turn multimodal data into a scalable workflow for model evaluation, debugging, and improvement

Who this is for:

Robotics and autonomous vehicle teams building or deploying end-to-end Physical AI systems
Machine learning engineers working with synchronized camera, LiDAR, radar, or sensor data
Teams responsible for debugging, evaluating, or improving Physical AI model behavior
Data scientists and ML engineers who need to find and analyze rare failure modes at scale