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Robots can now adapt to unforeseen errors and dynamically replan trajectories in real-time by simply incorporating sparse, human-provided or planner-provided "referring points" into their visuomotor policies.
VLA models, seemingly robust, crumble when faced with diverse linguistic variations, as a new red-teaming approach reveals a staggering drop in task success from 93% to just 6%.
