SCCRUB: Surface Cleaning Compliant Robot Utilizing Bristles
Jakub F. Kowalewski, Keeyon Hajjafar, Alyssa Ugent, and Jeffrey Ian Lipton

TL;DR
This paper presents a soft robotic arm capable of effectively scrubbing surfaces by exerting continuous torque, achieved through a neural network-based inverse kinematics model, enabling safe and efficient cleaning of adhered residues.
Contribution
The work introduces a neural network trained to control a soft robot arm's force and position, enabling it to perform scrubbing tasks traditionally difficult for soft robots.
Findings
Successfully scrubbed burnt food residue and sticky substances with 99.7% contamination removal
Demonstrated soft robot's ability to exert continuous torque safely around humans
Neural network enabled open-loop force and position control for effective scrubbing
Abstract
Scrubbing surfaces is a physically demanding and time-intensive task. Removing adhered contamination requires substantial friction generated through pressure and torque or high lateral forces. Rigid robotic manipulators, while capable of exerting these forces, are usually confined to structured environments isolated from humans due to safety risks. In contrast, soft robot arms can safely work around humans and adapt to environmental uncertainty, but typically struggle to transmit the continuous torques or lateral forces necessary for scrubbing. Here, we demonstrate a soft robotic arm scrubbing adhered residues using torque and pressure, a task traditionally challenging for soft robots. We train a neural network to learn the arm's inverse kinematics and elasticity, which enables open-loop force and position control. Using this learned model, the robot successfully scrubbed burnt food…
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Taxonomy
TopicsSoft Robotics and Applications · Micro and Nano Robotics · Advanced Sensor and Energy Harvesting Materials
