Abstract
Robotic grippers aim to replicate the remarkable functionalities of the human hand by providing advanced perception, adaptability, stability, and dexterity for complex tasks. Achieving these capabilities demands a sophisticated design hierarchy and robust perception mechanisms that ensure accurate manipulation. This paper introduces Active Soft Polyhedral Networks (ActiveSPN), a gripper design that leverages an active, non-biomimetic surface for precise in-hand manipulation. A vision system integrated directly into the fingers further facilitates accurate pose estimation of the in-finger object. The proposed system includes: (i) a soft polyhedral network featuring a transparent active belt to deliver complete three-dimensional adaptation and dexterous in-finger motion, and (ii) a generative learning-based pipeline for in-finger pose estimation. Experimental results demonstrate the ability of ActiveSPN to execute multi-degree-of-freedom in-finger manipulations, including two-axis rotation and one-axis translation. Moreover, the integrated vision-based pose estimation provides robust, real-time predictions, supporting consistent closed-loop control. Across diverse objects, the system achieves mean translational errors of 2.59 mm and rotational errors of 7 degrees, highlighting a promising paradigm for compact, efficient, and dexterous robotic manipulation. Codes are available at https://github.com/ancorasir/ActiveSPN.
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@article{Li2025ActiveSPN,
title = {ActiveSPN: Active Soft Polyhedral Networks with Pose Estimation for In-Finger Object Manipulation},
author = {Sen Li and Chengxiao Dong and Chaoyang Song and Fang Wan},
url = {https://github.com/ancorasir/ActiveSPN},
doi = {10.1109/LRA.2025.3583616},
year = {2025},
date = {2025-06-16},
urldate = {2025-06-16},
journal = {IEEE Robotics and Automation Letters},
volume = {10},
number = {8},
pages = {8115 - 8122},
abstract = {Robotic grippers aim to replicate the remarkable functionalities of the human hand by providing advanced perception, adaptability, stability, and dexterity for complex tasks. Achieving these capabilities demands a sophisticated design hierarchy and robust perception mechanisms that ensure accurate manipulation. This paper introduces Active Soft Polyhedral Networks (ActiveSPN), a gripper design that leverages an active, non-biomimetic surface for precise in-hand manipulation. A vision system integrated directly into the fingers further facilitates accurate pose estimation of the in-finger object. The proposed system includes: (i) a soft polyhedral network featuring a transparent active belt to deliver complete three-dimensional adaptation and dexterous in-finger motion, and (ii) a generative learning-based pipeline for in-finger pose estimation. Experimental results demonstrate the ability of ActiveSPN to execute multi-degree-of-freedom in-finger manipulations, including two-axis rotation and one-axis translation. Moreover, the integrated vision-based pose estimation provides robust, real-time predictions, supporting consistent closed-loop control. Across diverse objects, the system achieves mean translational errors of 2.59 mm and rotational errors of 7 degrees, highlighting a promising paradigm for compact, efficient, and dexterous robotic manipulation. Codes are available at https://github.com/ancorasir/ActiveSPN.},
note = {Accepted},
keywords = {Authorship - Co-Author, JCR Q1, Jour - IEEE Robot. Autom. Lett. (RA-L)},
pubstate = {published},
tppubtype = {article}
}
