Comprehensive evaluation of 89 policies showing optimal co-training practices mixing real robot data with sim/egocentric human videos to boost diversity and performance in large robotics foundation models.

Novel memory system preserving dynamic object identity and motion continuity across occlusions in video world models, addressing frozen/vanishing issues for improved predictive physics in embodied AI.

Massive dataset enabling dynamic world models with explicit states and actions, supporting predictive modeling for cross-embodiment robotic control.

New benchmark assessing world models on interaction tasks, pushing predictive physics and video modeling towards robotics applications with action-conditioned evaluation.

Unified MLLM framework with Sparse 3D VQ-VAE (70% token reduction) for part-level mesh decomposition and kinematic chain prediction, enabling physics-based robotic simulation from monolithic assets.

Closed-loop egocentric simulator persistently updating 3D scene state to generate spatially consistent interaction videos for continuous simulation, enabling cross-embodiment transfer from human videos to robotic manipulation tasks.

AI-trained capabilities for new whole-body motions using mocap/teleop data and sim-to-real reinforcement learning, deployable overnight on hardware.

Diffusion-based humanoid navigation trained solely on 5 hours of human egocentric video data, enabling zero-shot deployment on Unitree G1 for complex behaviors like handling glass walls, crowds, and dynamic obstacles via 360° visual memory and hybrid trajectory sampling; upcoming release of dataset, models, and code.

Comprehensive open-source agentic robotics framework treating VLMs/LLMs as code-generating APIs for perception (SAM3, Molmo) and control (IK, grasping), with CaP-Gym benchmark of 187 diverse manipulation tasks (tabletop, bimanual, mobile; sim/real) and CaP-Bench evaluating 12 frontier models; demonstrates rapid RL gains (7B model from 20% to 72% success) with strong sim-to-real transfer.

We've created GEN-1, our latest milestone in scaling robot learning. We believe it to be the first general-purpose AI model that crosses a new performance threshold: mastery of simple physical tasks. It improves average success rates to 99% on tasks where previous models achieve 64%, completes tasks roughly 3x faster than state of the art, and requires only 1 hour of robot data for each of these results. GEN-1 unlocks commercial viability across a broad range of applications—and while it cannot solve all tasks today, it is a significant step towards our mission of creating generalist intelligence for the physical world.