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By preserving the semantics of pretrained models while achieving superior compositional generalization, InternVLA-A1.5 redefines how robots can learn and execute complex tasks.

UMI-Bench 1.0 reveals that standardized real-world evaluations can dramatically improve the reliability of UMI-style robotic manipulation policies.

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%.