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A novel data synthesis approach enables a lightweight model to outperform state-of-the-art planning systems by over 10x in household appliance manipulation tasks.
Real-robot trials are costly and slow, but DataLadder enables scalable evaluations and data generation through a seamless interplay between robots, simulations, and human feedback.
Bridging the gap between human manipulation and robotic control, JoyAI-RA unlocks enhanced cross-embodiment behavior learning through multi-source pretraining.
