NVIDIA

PSYONIC’s Ability Hand has become a native asset in NVIDIA Isaac Lab, introducing a "real-to-real" transfer pipeline that uses human-driven data to train dexterous robotic manipulation.

Texas Instruments integrates mmWave radar with NVIDIA Jetson Thor to bridge the gap between AI simulation and real-world deployment, targeting the "last mile" of robotic safety.

NVIDIA researchers have revealed EgoScale, a framework that leverages a massive 20,854-hour egocentric human dataset to train robots in complex, fine-grained manipulation with minimal robot-in-the-loop data.

NVIDIA has released SONIC, a generalist humanoid controller trained on 100 million frames of motion data, aiming to replace manual reward engineering with a scalable "System 1" foundation for whole-body movement.

NVIDIA GEAR Lab has released DreamDojo, an open-source world model pretrained on a massive 44,000-hour dataset of human egocentric videos. By using "latent actions" to bridge the gap between human and robot movement, the model achieves zero-shot generalization and real-time controllability for teleoperation and planning.