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A single 500-hour visual-tactile dataset boosts zero-shot contact prediction from 13% to 38% IoU and quadruples robot manipulation success rates, proving scale and consistency are paramount for embodied learning.
Fusing volumetric and surface brain MRI data in a self-supervised autoencoder uncovers substantially more heritable genetic loci than unimodal approaches.
Robotic agents can now learn complex manipulation strategies from just a few demonstrations by treating them as evidence to be analyzed, not just training data.
Conditioning video world models on continuous tactile dynamics cuts hand-motion underestimation from 23% to 9%鈥攁nd surprisingly boosts visual prediction accuracy even when touch sensors are removed at inference.
AnaDiffusion allows for precise and controllable editing of 3D brain MRIs, achieving the lowest FID scores while preserving anatomical integrity.
Generating missing multi-view data from diverse driving videos boosts closed-loop driving robustness in edge cases by over 30%.
Embodied navigation agents, already struggling, fall apart when faced with the kinds of messy, real-world sensor and instruction corruptions that NavTrust now exposes.
Unlock human-like spatial reasoning in VLMs with VLM-3R, which reconstructs 3D understanding from monocular video using instruction tuning, bypassing the need for external depth sensors.
