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Achieve real-time 360掳 robotic vision with RobotPan, which drastically reduces the number of Gaussians needed for reconstruction and view synthesis compared to previous feed-forward methods.
Humanoid robots can now recover from extreme perturbations with more natural, human-like movements by using a diffusion model as a learned intermediary between high-level commands and low-level motor control.
By combining simulation-trained priors with real-world adaptation, SPARR achieves near-perfect success in robotic assembly without human supervision, outperforming both sim-to-real and real-world RL baselines.
Humanoid robots can now learn complex, terrain-aware motions directly from video using a low-cost pipeline, eliminating the need for expensive MoCap data and manual motion design.
