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A simulation flywheel recursively bootstraps embodied data, generating richer scenes and tasks for robot pretraining and evaluation.
A unified architecture achieves SOTA embodied AI by jointly learning physical reasoning, world generation, and action prediction, outperforming prior models in generalization and long-horizon tasks.
A function-preserving Real-to-Sim-to-Real framework that generates synthetic demonstrations from reconstructed assets without teleoperated source trajectories is proposed, enabling robust generalization across unseen object geometries and diverse visual conditions in contact-rich and long-horizon tasks.
SSMB sets a new standard in keypoint detection under motion blur, achieving superior performance without relying on deblurring or handcrafted features.
EWR boosts UAV delivery success rates by effectively navigating the unpredictable challenges of wind, transforming how we approach energy management in aerial logistics.
By integrating semantic foresight from vision-language models, SG-WAM ensures that robotic actions are not just visually accurate but also linguistically aligned, transforming how robots interpret and execute tasks.
