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Robots can now achieve over double the success rate in using novel tools by leveraging keypoint trajectory reasoning for functional generalization.
Spatial-semantic prompting outperforms traditional text-only methods in embodied visual tracking, especially in complex environments with similar distractors.
Humanoid robots can now achieve superior stability and accuracy in unpredictable environments by leveraging a physics-based disturbance observer that operates without force sensors.
Gesture understanding can elevate robotic manipulation success rates by 80%, transforming how robots interpret human intentions.
Robots can now understand and act on your gaze while following language commands, enabling more intuitive and precise human-robot collaboration.
Robots can now better understand implicit instructions in cluttered scenes, choosing the knife over the scissors when told to "cut the apple," thanks to a new benchmark and neural network architecture designed to handle functionally similar objects.
