Embodied-ai

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.

A new full-stack framework from HKUST and Shanghai AI Lab allows humanoid robots to acquire complex athletic skills like basketball and reactive fighting directly from human videos—no manual reward engineering required.

Humanoid has unveiled KinetIQ, a multi-layered AI architecture designed to orchestrate entire robot fleets across retail, industrial, and home environments using a cross-timescale approach.

In a deep dive on the Robo Papers podcast, 1X Director of Evaluations Daniel Ho explains how "imagination" via video generation is allowing humanoids to perform zero-shot tasks with minimal robot-specific training data.

NVIDIA GEAR Lab has unveiled DreamZero, a 14-billion parameter World Action Model (WAM) that uses video diffusion to grant robots physical "imagination," enabling zero-shot task completion and rapid adaptation across different robotic embodiments.