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Despite high success rates for isolated skills, robots frequently stall in long-horizon tasks due to semantic handoff failures, revealing a hidden challenge in skill composition.
Many robotic policies that seem successful in manipulation tasks actually compromise safety, with SoftVTBench revealing a stark contrast between goal completion and physical safety metrics.
Species identification and discovery, traditionally treated as separate problems, can be unified into a single framework that leverages retrieval-augmented reasoning for improved accuracy and interpretability.
