GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators

TL;DR

GELLO is an affordable, easy-to-assemble teleoperation framework that improves data collection for robot manipulation learning, demonstrated on multiple robotic arms with open-source hardware and software.

Contribution

The paper introduces GELLO, a low-cost, intuitive teleoperation system with kinematic matching, enhancing demonstration quality and efficiency for imitation learning in robotics.

Findings

GELLO outperforms VR controllers and 3D spacemouses in user studies.

It enables complex bi-manual and contact-rich manipulation tasks.

GELLO is adaptable to multiple robotic arms with open-source resources.

Abstract

Humans can teleoperate robots to accomplish complex manipulation tasks. Imitation learning has emerged as a powerful framework that leverages human teleoperated demonstrations to teach robots new skills. However, the performance of the learned policies is bottlenecked by the quality, scale, and variety of the demonstration data. In this paper, we aim to lower the barrier to collecting large and high-quality human demonstration data by proposing a GEneraL framework for building LOw-cost and intuitive teleoperation systems for robotic manipulation (GELLO). Given a target robot arm, we build a GELLO controller device that has the same kinematic structure as the target arm, leveraging 3D-printed parts and economical off-the-shelf motors. GELLO is easy to build and intuitive to use. Through an extensive user study, we show that GELLO enables more reliable and efficient demonstration collection compared to other cost efficient teleoperation devices commonly used in the imitation learning literature such as virtual reality controllers and 3D spacemouses. We further demonstrate the capabilities of GELLO for performing complex bi-manual and contact-rich manipulation tasks. To make GELLO accessible to everyone, we have designed and built GELLO systems for 3 commonly used robotic arms: Franka, UR5, and xArm. All software and hardware are open-sourced and can be found on our website: https://wuphilipp.github.io/gello/.