  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/.
