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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.
IMPACT is introduced, a scalable Interaction-aware Model training framework with Prior-guided Attention Calibration and Targeting, which consistently outperforms the corresponding MSE-trained baselines, improving interaction fidelity, physical plausibility, and visual quality.
Causal Action Effect Reweighting (CAER), a general training paradigm that redistributes supervision toward the tokens whose predicted future is causally affected by the action, is introduced.
WorldScape Policy 2.0 is introduced, a controllable WAM with reasoning-augmented long short-term memory and fine-grained instruction following and in-context adaptation that demonstrates superior capabilities in long-horizon autonomous planning, fine-grained instruction following and in-context adaptation.
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