Robots rarely fail at step 1. They fail at step 14, when the task needs context, order and judgment. That’s what long-horizon data is for. We capture full human tasks from start to finish, in real homes and commercial kitchens. That includes the mistakes and corrections, so models learn the donts as well as the dos. Every episode is densely annotated for: → Context-driven task inference → Spatial, ordinal and negation grounding → External knowledge applied to action If you source training data for VLA or world models, check the comment below. If you source training data for VLA or world models, read the comment below.

Sneak peek at who's going to at our research session this Saturday: → An Adobe researcher who worked on egocentric hand tracking at CMU → A former CTO who built robots to help astronauts in space → A builder making mocap gloves that can sense touch → A PhD researcher at IISc's HiRO Lab An evening with our head researcher Dr. Sandeep, figuring out all things egocentric hand tracking and where gaps are. Building in this space? Grab your spot: @ishank20 @manishdiesel

This Saturday, our head researcher Dr. Sandeep Reddy Sabbella is leading a closed-door session at our Bangalore office on why egocentric hand tracking breaks. He's seen this problem from both ends. During his PhD at Sapienza, field data was extremely difficult to collect with environmental changes and agricultural crop cycles. Hence they built VR simulation to replicate the data that was collected for conducting experiments. At Humyn Labs, he works with first-person footage of hands cooking, folding and fixing things. Hands leave the frame, wrap around objects, and the camera never sits still. He'll share how we're tackling it. Then India's top researchers in hand pose, egocentric vision and robot learning take it from there. Sat, 24th Oct. Indiranagar. Last 5 spots remaining. Grab yours below 👇

This Saturday, we're debating an interesting question at Humyn Labs. You have a limited budget to collect hand data for physical AI. What do you spend it on? 10,000 more clean demonstrations of pick-and-place and grasping? Or a few hundred hard sequences: fingers self-occluded or hidden behind the object, hands truncated at the frame edge, off-nominal camera viewpoints, rare grasp types? We'll tackle this in a closed-door research session led by Dr. Sandeep. 30 curated seats. 24th Oct. Indiranagar. If you work on hand pose, egocentric vision or robot learning, this is for you. Link in the comment below.

Physical AI won't work in a lab. It'll work in the real world. So we build its training data from real people in 20+ countries, home to 39% of the world's population. Hiring engineers and researchers to scale it to petabytes of vision, speech, touch and motion data. Roles ↓

We’re hosting a closed-door research session in Bengaluru this Saturday, with researchers from India’s top labs working on hand pose, egocentric vision and robot learning. The core question: Why does egocentric hand tracking break in the wild? Over the evening, we’ll: - Map where hand tracking fails using real cases - Check whether today’s benchmarks measure what robots actually need - Share how we’re approaching it, and learn together The session will be moderated and led by our Lead Researcher Sandeep Reddy Sabbella, PhD. What the room figures out shapes our next round of egocentric data. Sat, 24th Oct. Indiranagar. Limited to 30 researchers. Apply below 👇