NVIDIA's new CHORD framework teaches robot hands to manipulate objects by matching how human contact moves an object. Learned from human demos and transferred to real dexterous hands. Look at the high success rates: 82.12% success across 1,831 contact-rich tasks; 90.77% success on whole-body manipulation. With policies deployed from simulation. That's the #SharpaWave in the loop :) Project: nvidia-isaac.github.io/video_to_data/… Paper: nvidia-isaac.github.io/video_to_data/… #PhysicalAI #DexterousManipulation #RobotLearning #NVIDIA #EmbodiedAI #Robotics #Dexteroushands

@SharpaRobotics The interesting signal here is contact. Hands are not just moving through space; they are applying force, creating torque, correcting slips, and changing object state. That’s the kind of human-object interaction data robotics needs more of.