Fed-HANet: Federated Visual Grasping Learning for Human Robot Handovers

Ching I. Huang, Yu Yen Huang, Jie Xin Liu, Yu Ting Ko, Hsueh Cheng Wang, Kuang Hsing Chiang, Lap Fai Yu

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Human-robot handover is a key capability of service robots, such as those used to perform routine logistical tasks for healthcare workers. Recent algorithms have achieved tremendous advances in object-agnostic end-to-end planar grasping with up to six degrees of freedom (DoF); however, compiling the requisite datasets is simply not feasible in many situations and many users consider the use of camera feeds invasive. This letter presents an end-to-end control system for the visual grasping of unseen objects with 6-DoF without infringing on the privacy or personal space of human counterparts. In experiments, the proposed Fed-HANet system trained using the federated learning framework achieved accuracy close to that of centralized non-privacy-preserving systems, while outperforming baseline methods that rely on fine-tuning. We also explores the use of a depth-only method and compares its performance to a state-of-the-art method, but ultimately emphasizes the importance of using RGB inputs for better grasp success. The practical applicability of the proposed system in a robotic system was assessed in a user study involving 12 participants. The dataset for training and all pretrained models are available at https://arg-nctu.github.io/projects/fed-hanet.html.

Original languageEnglish
Pages (from-to)3772-3779
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume8
Issue number6
DOIs
Publication statusPublished - Jun 1 2023

Keywords

  • Federated learning
  • human-robot interaction
  • service robots

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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