the most famous egocentric datasets are people cooking in their own kitchens. the robots we're training on them are headed for warehouses, garages, and factory floors apac egocentric stereo is 12 first-person recordings of people actually doing their jobs: an automotive garage, a construction site, an electronics factory, a bar, a shipment hub, a laundromat head-mounted stereo rig, 1920x1080 per eye at 30 fps, plus a depth render, hand and head tracking, and a caption for what the wearer is doing at every moment. 248 segments spanning 62 distinct verbs checkout the dataset parsed into fiftyone format. every stream scrubs on one shared timeline in fiftyone: both eyes, depth, tracking, and captions together, one line to load checkout the dataset here: or just jump right in with the hugging face space hosting the dataset:

Most robot datasets store one sentence per episode: the task. Real problems need more: a plan, a memory, room to answer questions and change course. We just shipped full language support in the LeRobot dataset format. Now lerobot-annotate points a vision-language model at each episode and writes the subtasks, plans, memory, and grounded Q&A itself - roughly $1.20 for 100 episodes. From there, recipes turn the annotated rows into policy-agnostic training messages, so the same dataset can teach planning, memory, tool calls, or low-level control, no dataset redesign required. Read how it all works on the blog:

Gemini 4 Argon combined three open-source datasets to build an interactive 3D visualization of human anatomy. Toggle different layers in motion, isolate muscles, or zoom inside a beating heart.

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