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:

Over 4 petabytes of models and datasets have been uploaded to HF just last week by AI builders and their agents! That's the equivalent of ~800,000 HD movies. More than ever the storage and collaboration platform for AI!

An MCP server that lets AI assistants search and analyze datasets from France's national open data platform directly through conversation. - Public instance available at with no access restrictions - Works with Claude, ChatGPT, Gemini, Mistral, and 13+ other clients - Ask questions in plain French like "Quels jeux de données sont disponibles sur les prix de l'immobilier ?" - Covers search, exploration, and analysis of datasets Explore it here:

🧵 New repo: Awesome Egocentric Dataset — a curated list of first-person vision datasets, refreshed beyond the classic Ego4D-era list. ✅ Recent 2023–2026 releases (Ego-Exo4D, EgoSchema, AEA, EgoExoLearn, EgoLife…) ✅ Dead/broken links removed, migrated ones fixed ✅ A section on egocentric-driven VLA papers (training source + data-processing), with first-principles + fishbone analysis 🚀 Open-source, contributions welcome. 🔗 #EgocentricVision# #FirstPersonVision# #VLA# #ComputerVision# #Datasets#

A gripper with two thumbs! 🐨 Handheld data collection is how a lot of manipulation datasets get built now. Someone picks up a gripper-shaped device, does the task by hand, and the recording becomes training data. Voila! 🤌🏼 The catch is that these devices are usually shaped to match whatever robot gripper already exists. The human ends up working around the robot's morphology, which costs both ergonomics and the quality of the demonstration being recorded. The @rai_inst team designed The Koala Gripper. It includes two devices with full parity: a capture device a person holds, and a robotic device that executes the learned policy. Anything you can do with one, the other reproduces. The shared feature set gets updated as the design iterates, so neither side quietly compromises the other. The morphology is where it gets interesting. Two independently controlled underactuated fingers are opposed by a single pivoting "dual thumb", which is where the koala name comes from. → Each finger is a 9-bar linkage with one actuated and one underactuated degree of freedom, with the spring sitting proximal to the phalanges to drive preshaping → Fingernails on the distal link for precise pinches, contoured pads for secure wrap grasps → The handheld trigger traces a natural fingertip arc instead of a straight line, lengthening the stroke and giving the operator better mechanical advantage → On the robot side, a frameless motor with an integrated high-pitch ballscrew keeps reflected inertia low, so the fingers stay backdrivable at an effective mass of tens of grams The dual thumb is what buys tool stabilising, singulating one object out of a pile, and tabletop pinches. Congrats to @zubinbot and the RAI Institute team behind it 👏🏼 🔗 Project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →