Visualizing and Debugging Robot Learning Data with Voxel51 and Hugging Face's LeRobot

Robot learning data doesn't look like the data that trains most vision-language models. It's temporal, low-dimensional in the parts that matter most, embodiment-specific, and expensive to collect — and increasingly, it's shipped in Hugging Face's LeRobot format. But packaging robot data consistently isn't the same as being able to see it: episodes hide broken metadata, silently dropped sensor modalities, and mismatched schemas between robots that look identical on paper. This workshop is a hands-on tour of what actually breaks when you load real LeRobot datasets — and how Voxel51's native LeRobot support in FiftyOne catches it. Starting from the core differences that make robot data hard (temporal structure, proprioception, action grounding, embodiment heterogeneity, data scarcity), we'll move into live debugging: a metadata bug that broke multiple dataset imports, sensor streams that vanish without an error, and two robots running an identical benchmark whose data is structurally incompatible. By the end, you'll know what to check before you trust a LeRobot dataset, and how to actually look inside one. Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in VLMs, Visual Agents, Document AI, and Physical AI. * Voxel51 - https://voxel51.com * Connect with Harpreet on LinkedIn - https://www.linkedin.com/in/harpreetsahota204/ #FiftyOne #Voxel51 #ComputerVision