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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.
CAER redefines how we train world models by ensuring that only the most relevant tokens drive learning, leading to substantial gains in video generation quality.
Recalibrating supervision in world model training can drastically enhance interaction fidelity and physical plausibility without the need for external representations.
Bridging the gap between human and robotic manipulation, HandEdit enables scalable learning for dexterous robotics using abundant egocentric video data.
WorldScape Policy 2.0 achieves unprecedented long-horizon autonomous planning by integrating reasoning-augmented memory with multimodal instruction processing.
