Ipshita D

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 👇

Every robot follows instructions. Until it meets a cat. We teach robots the way humans learn - frame by frame, joint by joint, from our egocentric footage. It also learned what every human in that kitchen knew: it's too hard to say "no" to a hungry cat 🤷‍♂️ Purr intended.

We're Humyn Labs. With us, every robot works fine. We turn human experience into robot skills built from thousands of hours of model-ready data. Unlike LLMs, that learnt from the entire internet, robotics data doesn't get that shortcut. Robots need real-world experience to learn how to: - Iron clothes with a toddler underfoot and a dog at your heels - Restock shelves while navigating a crowded, constantly changing store - Assemble components amid the variability of a real factory floor That experience happens once, in the physical world, and disappears. We’re building the data platform Physical AI learns from: four sensory modalities, real-world environments, and the human experience needed to turn moments into skills.