Today, we're kicking off the first phase of the research preview for Model Hardware Standard (MHS): a new standard for AI agents to safely operate physical equipment in scientific research and advanced manufacturing.
Read more: https://t.co/XQ2y9EW7Af
Meta just open-sourced its dexterity stack! 🪬
Most physics simulators were built for things that move through space.
Walking robots, drones, cars. Contact is the part they approximate worst, and obviously dexterous manipulation is nothing but contact.
Project SuperDex, from Meta Reality Labs Research, is built the other way around, a contact-first physics engine with the whole platform stacked on top of it.
The cool part is that it's on GitHub.
The engine runs one solver across rigid bodies, soft bodies, rods and tendons, shells and cloth, in the same model.
→ Non-convex collision with accurate contact force distributions, so a multi-finger grasp gets simulated rather than approximated
→ Tactile sensors and soft contact as first-class primitives
→ Numerical stability without the tight time-step limits explicit solvers force on you
→ Constraint-aware inverse kinematics running on the same optimization core as the forward dynamics
Then the data layer. Put on a Quest 3, teleoperate the simulated hand with haptic feedback, and generate demonstration datasets without touching real hardware.
They show a shape-sorting policy trained entirely in simulation and deployed zero-shot on a real robotic hand.
Robot hands are getting good. Data for them isn't that fast.
Meta is betting the cheapest way to collect contact-rich demonstrations is a headset people already own, pointed at a simulator instead of a game.
🔗 Here's the project page: https://t.co/bFCmZrp4jA
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