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TurboVLA achieves 97.7% success in robotic manipulation while using less than 1 GB of VRAM, challenging the dominance of LLM-centric approaches.
Current vision-language-action models falter in dynamic robotic manipulation due to limited data and poor spatiotemporal reasoning, but a new dataset and architecture close the gap.
