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Many manipulation tasks, such as placement or within-hand manipulation,require the object's pose relative to a robot hand. The task is difficult whenthe hand significantly occludes the object. It is especially hard for adaptivehands, for which it is not easy to detect the finger's configuration. Inaddition, RGB-only approaches face issues with texture-less objects or when thehand and the object look similar. This paper presents a depth-based framework,which aims for robust pose estimation and short response times. The approachdetects the adaptive hand's state via efficient parallel search given thehighest overlap between the hand's model and the point cloud. The hand's pointcloud is pruned and robust global registration is performed to generate objectpose hypotheses, which are clustered. False hypotheses are pruned via physicalreasoning. The remaining poses' quality is evaluated given agreement withobserved data. Extensive evaluation on synthetic and real data demonstrates theaccuracy and computational efficiency of the framework when applied onchallenging, highly-occluded scenarios for different object types. An ablationstudy identifies how the framework's components help in performance. This workalso provides a dataset for in-hand 6D object pose estimation. Code and datasetare available at: https://github.com/wenbowen123/icra20-hand-object-pose

Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands