MIT researchers developed VLASH, a method that lets robots plan their next movements while completing current actions, cutting pauses and doubling task speeds in some tests. The system predicts a robot’s future state so vision-language-action models can prepare the next action sequence without relying on stale observations, accelerating reaction times more than 30-fold. VLASH maintained 90% accuracy in a cube-sorting test, sped training fivefold without added computing overhead and is being extended with world models for more dynamic environments. Research backed by the MIT-IBM Computing Research Lab, Amazon, the National Science Foundation and Nvidia has produced a method that lets robots plan their next action while completing the current one, reducing pauses and doubling execution speeds in several tests. The technique, developed by researchers at MIT and collaborating institutions, allows an AI model to forecast where a robot will be after its current movement ends. According to MIT, the model can then prepare the next movement before the robot reaches that position, producing smoother motion and faster reactions. MIT said the approach, called VLASH, accelerated robotic reaction speeds more than 30-fold by eliminating much of the delay between planned groups of actions. In a cube-sorting test, the system finished the task twice as fast as leading comparison methods while maintaining a 90% success rate. The researchers also tested VLASH on robotic arms performing stacking, sorting and pick-and-place work, as well as faster activities such as table tennis and whack-a-mole. The method could eventually support robots