RoboCAP: Robotic Classification and Precision Pouring of Diverse Liquids and Granular Media with Capacitive Sensing
Yexin Hu, Alexandra Gillespie, Akhil Padmanabha, Kavya Puthuveetil, Wesley Lewis, Karan Khokar, Zackory Erickson
Abstract
Liquids and granular media (e.g., oats, rice, lentils) are pervasive throughout human environments, yet remain challenging for robots to sense and manipulate precisely. In this work, we present a systematic approach to integrating capacitive sensing within robotic end effectors, enabling robust sensing and precise manipulation of liquids and granular media. We introduce the parallel-jaw RoboCAP Gripper with embedded capacitive sensing arrays that enable a robot to directly sense the materials and dynamics of liquids inside diverse containers. Our system achieves 82.8% classification accuracy across 81 container–substance combinations, and enables a robotic manipulator to perform precision pouring with a mean error of 3.2g over 200 trials. Code, designs, and build details are available on the project website <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.
BibTeX
@inproceedings{iros2025_robocaproboticcl,
title = {RoboCAP: Robotic Classification and Precision Pouring of Diverse Liquids and Granular Media with Capacitive Sensing},
author = {Yexin Hu and Alexandra Gillespie and Akhil Padmanabha and Kavya Puthuveetil and Wesley Lewis and Karan Khokar and Zackory Erickson},
booktitle = {IROS 2025},
year = {2025}
}