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INSPECT is introduced, which learns robot view preferences from records of a smart-glasses assistant that answers part queries and provides next-step guidance and achieves the highest view utility among the compared non-oracle policies and raises human-rated full verifiability.
RAFAIL is introduced, a framework for detecting execution failures during robotic manipulation that requires no failure data and achieves 73.4% balanced accuracy across three real-world robotic manipulation tasks, outperforming the strongest evaluated OOD- and uncertainty-based baselines.
MS-MEM achieves higher mapping accuracy while minimizing scene disturbances, showcasing the power of integrating multiple manipulation skills in robotic perception.
Decomposing pose estimation into error attribution and targeted mitigation allows simple algorithms like ICP to rival the robustness of complex foundation models, but with significantly less computation.
