MIT researchers built VLASH, a system that lets robots plan their next move while still executing the current one, instead of stopping to replan. It roughly doubles speed on pick-and-place tasks and shows bigger gains on fast, continuous tasks like table tennis and Whack-a-Mole, with no added compute cost or special hardware needed. A companion technique, action quantization, pushes speed further (1.5–2x) at a small accuracy cost. It's not a fix for every scenario, unpredictable or contact-heavy environments still need more testing, but it targets a bottleneck that's slowed robots down for years.
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