ActiveSPN: Active Soft Polyhedral Networks With Pose Estimation for In-Finger Object Manipulation

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 letter 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$^\circ$, highlighting a promising paradigm for compact, efficient, and dexterous robotic manipulation.