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WLA$^3$ (World Latent Action Modeling for Semantics, Dynamics, and Kinematics), a unified generalist policy model framework built around representations learned by a World Latent Action Model (WLAM), is introduced.
A transferable residual adapter that injects additional ranking-specific features into ranking in a residual manner and an asymmetric multi-epoch training strategy that resets sparse parameters while continuously accumulating dense parameters across epochs, alleviating the overfitting of sparse parameters are proposed.
SurfSVR transforms 2D surface information into robust 3D structures, eliminating common artifacts and achieving unprecedented reconstruction quality.
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.
LOGOS shows that a single generative model can outperform specialized systems across a range of scientific tasks, challenging the need for separate AI frameworks in the natural sciences.
By explicitly aligning visuomotor representations with the action space's rigid-body geometry, OASIS achieves superior robotic manipulation performance compared to methods relying on implicit geometry recovery.
Bridging the gap between human manipulation and robotic control, JoyAI-RA unlocks enhanced cross-embodiment behavior learning through multi-source pretraining.
