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Achieving 82.9% success in articulated object manipulation with only half the data, KAI redefines efficiency in robotic learning.
RoboInter1.5 reimagines intermediate representations as a powerful interface that enhances robotic reasoning and execution, bridging the gap between low-level actions and high-level world dynamics.
By preserving the semantics of pretrained models while achieving superior compositional generalization, InternVLA-A1.5 redefines how robots can learn and execute complex tasks.
Models with similar success rates can exhibit vastly different strengths and weaknesses, revealing the hidden complexities of mobile manipulation capabilities.
Robots can now perform contact-rich tasks with significantly improved success rates and reliability by explicitly reasoning about forces, outperforming prior methods by up to 48%.
Bimanual robots can now achieve robust dexterous grasping in the real world, thanks to a massive 20M-frame synthetic dataset and a simple attention-based policy that transfers surprisingly well.
