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Adaptive representations in functional gradient descent can achieve global convergence guarantees while significantly enhancing optimization efficiency and accuracy.
Label-free consistency training can elevate spatial reasoning in LRMs to levels comparable with traditional supervised methods, revolutionizing how we approach model training.
VLMs can learn to actively reason and plan in 3D environments by distilling view graphs from self-exploration trajectories, enabling them to surpass even larger models like GPT-4 Pro and Gemini 1.5 Pro on interactive view planning.
Forget tedious calibration – DOT-Sim lets you train tactile perception policies in simulation and deploy them directly to real robots with impressive accuracy, thanks to its physically accurate and rapidly calibrated model.
Unlock interactive digital twins from messy, real-world videos: FunRec automatically turns egocentric RGB-D recordings into simulation-ready 3D scenes.
Even state-of-the-art VLMs exhibit systematic failures in reasoning about the physical feasibility of actions in 3D environments, despite high semantic confidence.
Generate minute-long videos with compelling narrative structure and local realism, even with limited long-form training data, by cleverly combining supervised flow matching for global coherence with mode-seeking alignment to a short-video teacher for local fidelity.
