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Disentangled EFM Fine-Tuning (DEFT) is proposed, a plug-and-play adapter for EFM fine-tuning that improves matched fine-tuning baselines and encourages branch disentanglement of pretrained tokens into task-oriented content and subject-consistent style.
Bench2Dex is meant as a platform for studying visuo-tactile learning across dexterous hands and offers a shared setting for algorithm development while tactile hardware and simulation models are still evolving.
HandEdit serves as a critical resource at the intersection of image editing and robotics: it advances embodiment-aware editing models while enabling scalable dexterous robotic learning from abundant human video data, paving the way for more generalizable Embodied AI.
Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology, but EEG-PRIME achieves balanced accuracy comparable to within-session calibration models without target-domain optimization, calibration, or lin...
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