Soft Robotic Dynamic In-Hand Pen Spinning

Dynamic in-hand manipulation remains challenging for soft robotic systems, which have demonstrated advantages in safe, compliant interactions but struggle with highspeed dynamic tasks. In this work, we present SWIFT, a system for learning dynamic tasks using a soft and compliant robotic hand. Unlike previous works that rely on simulation, quasistatic actions, and precise object models, SWIFT learns to spin a pen through trial and error using only real-world data and without requiring explicit knowledge of the pen's physical attributes. With self-labeled trials sampled from the real world, SWIFT discovers the set of pen grasping and spinning primitive parameters that enables a soft hand to spin a pen reliably. After 130 sampled actions per object, SWIFT achieves 10/10 success rate across three pens with different weights and weight distributions, demonstrating generalizability and robustness to changes in object properties. The results highlight the potential for soft robotic end-effectors to perform dynamic tasks. We also demonstrate generalization to different shapes and weights, such as a brush and a screwdriver, with 10/10 and 5/10 success rates, respectively. Videos, data, and code are available at https://soft-spin.github.io.