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Scaling visuomotor context to 8K timesteps enables robots to master complex tasks and adapt in real-time, outperforming previous models by a staggering margin.
GaP outperforms traditional methods in variational automation tasks by leveraging directed computation graphs for real-time adaptability and improved success rates.
ASPIRE achieves a staggering 31% success rate on unseen long-horizon tasks, compared to just 4% for prior methods, highlighting its superior adaptability and efficiency.
A single generalist model outperforms specialized systems, achieving over 35% improvement in real-world robotic task success.
Coding agents can now autonomously refine robotic manipulation policies to achieve a staggering 99% success rate on complex tasks, revolutionizing real-world robotics.
Extracting action signals from 32,041 hours of human video enables CAIP to outperform leading vision encoders in robotic manipulation tasks by over 30%.
Forget painstakingly engineering robot behaviors: DreamZero learns directly from video of other robots or even humans, adapting to new tasks and bodies with just minutes of data.
