Current articulated object pose estimation methods largely rely on dense prediction for all the input observed point cloud that suffers from huge computational costs and inference time. Besides, self-occlusion is also becoming a key problem that limits the pose estimation performance for those child parts. To solve these issues, we propose a Reinforcement learning based Articulation Pose EstimatoR (ReAPER), which integrates RL into deep neural network for per-part pose estimation. Specifically, we design the novel action space that involves the object’s rotation and translation, as well as a reward function considering chamfer distance during pose fitting. To speed up the RL policy training, we employ imitation learning for policy initialization. Finally, we also introduce a new kinematic energy function to optimize the child parts’ poses. Experimental results show that ReAPER could obtain state-of-the-art performance on articulated object pose estimation task.

Object handover is a common human collaboration behavior that attracts attention from researchers in Robotics and Cognitive Science. Though visual perception plays an important role in the object handover task, the whole handover process has not been specifically explored. In this work, we propose a novel rich-annotated dataset, H2O, for visual analysis of human-human object handovers. The H2O, which contains 18K video clips involving 15 people who hand over 30 objects to each other, is a multi-purpose benchmark. It can support several vision-based tasks, from which, we specifically provide a baseline method, RGPNet, for a less-explored task named Receiver Grasp Prediction. Extensive experiments show that the RGPNet can produce plausible grasps based on the giver’s hand-object states in the pre-handover phase. Besides, we also report the hand and object pose errors with existing baselines and show that the dataset can serve as the video demonstrations for robot imitation learning on the handover task.