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
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 <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://arg-nctu.github.io/projects/fed-hanet.html</uri> .