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In a new interview, the Meta Chief AI Scientist and AMI Labs founder doubles down on world models, arguing that the massive data requirements of current humanoid demos prove the Vision-Language-Action (VLA) approach is a dead end.
Generalist AI CEO Pete Florence argues that terms like 'VLA' and 'World Model' are temporary crutches for the industry, revealing that GEN-1's 99% scratch-trained architecture is a bet on the eventual dominance of pure robotic data.
As "world models" become the dominant paradigm in robotics, the industry is grappling with a term that means everything and nothing. We map out the competing visions from LeCun, NVIDIA, 1X, and Tesla.
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.
Waymo has unveiled its new World Model, powered by Google DeepMind’s Genie 3, to simulate rare "long-tail" driving scenarios and edge cases, signaling a major shift toward generative world models in the race for physical AI.
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.
In a new interview, Google DeepMind's Carolina Parada discusses the 'agentic' two-part brain of Gemini Robotics 1.5, its 'surprising' ability to transfer skills between different robots, and why she believes the home will be 'one of the last frontiers' for the technology.
In a new technical deep-dive, Tesla's VP of AI and Optimus lead Ashok Elluswamy details the end-to-end neural network strategy for FSD, confirming the same "neural world simulator" and architecture will "seamlessly transfer" to its humanoid robot.
Speaking at MIT, Yann LeCun argued the "big secret" of the humanoid industry is that companies lack the fundamental AI breakthroughs—specifically "world models"—needed to make robots "generally useful" in domestic settings.
