AnyPos automates task-agnostic action collection and inverse-dynamics modeling with arm/end-effector decoupling plus a direction-aware decoder, delivering 51% higher test accuracy and 30-40% better success rates on bimanual tasks.
Deformable convolutional networks
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Feature sharing embedded in every stage of Cascade R-CNN narrows the low-IoU gap, improves all thresholds, and reaches 43.2 AP on COCO with negligible added parameters.
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AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation
AnyPos automates task-agnostic action collection and inverse-dynamics modeling with arm/end-effector decoupling plus a direction-aware decoder, delivering 51% higher test accuracy and 30-40% better success rates on bimanual tasks.
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Rethinking Classification and Localization for Cascade R-CNN
Feature sharing embedded in every stage of Cascade R-CNN narrows the low-IoU gap, improves all thresholds, and reaches 43.2 AP on COCO with negligible added parameters.