Authors:
Chaitanya Bandi
and
Ulrike Thomas
Affiliation:
Robotics and Human Machine Interaction Lab, Technical University of Chemnitz, Chemnitz, Germany
Keyword(s):
Hand Pose, Hand-Object Pose, Body Pose, Handover, Human-Robot Interaction.
Abstract:
Object handover is a fundamental task in human-robot interaction (HRI) that relies on robust perception features such as hand pose estimation, object pose estimation, and human pose estimation. While human pose estimation has been extensively researched, this work focuses on creating a comprehensive architecture to track and analyze hand and object poses, thereby enabling effective handover state determination. We propose an end-to-end architecture that integrates unified hand-object pose estimation with hand pose tracking, leveraging an early and efficient fusion of RGB and depth modalities. Our method incorporates existing state-of-the-art techniques for human pose estimation and introduces novel advancements for hand-object pose estimation. The architecture is evaluated on three large-scale open-source datasets, demonstrating state-of-the-art performance in unified hand-object pose estimation. Finally, we implement our approach in a human-robot interaction scenario to determine th
e handover state by extracting and tracking the necessary perception features. This integration highlights the potential of the proposed system for enhancing collaboration in HRI applications.
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