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Columbia University
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Achieving a staggering 90.4% reduction in robot-motion error, Hydra-0 redefines how we model and control robotic actions across varied environments.
Generating tactile signals from vision could revolutionize how robots manipulate objects in complex environments where touch is essential but hard to measure.
Automating real-to-sim conversion with vision-language agents could revolutionize how we simulate robotic interactions, making it faster and cheaper than ever before.
BoxTwin enables robots to accurately predict and adapt to the complex dynamics of elastoplastic articulated objects, revolutionizing manipulation in unstructured settings.
PGRD achieves superior accuracy in deformable object simulation by seamlessly blending physics-based principles with advanced neural corrections.
