CoRL 2026 Oral 🌟 A team led by Peking University introduced OpenDexGrasp. It takes free-form language instructions, multi-view images, and 3D point clouds as input. Then it directly generates a dexterous five-finger grasp pose. For example, grip a hammer by its handle, not its head. To train the model, the team built OpenDexVerse: • OpenDex-Scale: 1.24M automatically generated grasp samples. • OpenDex-Align: 27.42K human-teleoperated and transferred grasp samples. The results: In simulation: 62.96% functional grasp success on unseen object categories. The DexGraspNet 2.0 baseline scored 43.22%. On a real robot, it achieved a 72% success rate across 10 everyday objects, including brushes, shampoo bottles, umbrellas, and pitchers. The baseline scored 59%. It's also faster. Traditional methods predict the graspable area first, then optimize the hand pose. OpenDexGrasp generates the grasp pose directly. Traditional pipeline: 2.84 s OpenDexGrasp: 0.93 s 67% less inference time. It's not perfect, though. Thin or complex objects like keyboards, frisbees, and flower pots can still cause problems, including finger penetration and inaccurate grasp placement.