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A hierarchical framework that couples high-level multimodal reasoning with low-level function-constrained execution for deployable precision medical robotics, and introduces a scalable multi-agent pipeline to generate skill-oriented chain-of-thought data for structured training.
GigaBrain-0.7 achieves unprecedented task adaptability and completion rates, outperforming previous models in both home and industrial settings.
Action-only decoding in GigaWorld-Policy-0.5 slashes inference latency to 85 ms, revolutionizing real-time robot control efficiency.
HALO-WA boosts robotic manipulation success rates from 26.4% to 87.1% by effectively adapting to real-world errors in just over an hour of training.
Get simulation-ready assets for robotics and graphics in under a second, without any manual annotation, using a new feedforward approach that jointly learns physical attributes and 3D Gaussian Splatting reconstruction from a single video.
Robots can now plan 9x faster and achieve significantly higher success rates by decoupling action prediction from video generation in World-Action Models.
