  Open-sourced, user-friendly tools form the bedrock of scientific advancement
across disciplines. The widespread adoption of data-driven learning has led to
remarkable progress in multi-fingered dexterity, bimanual manipulation, and
applications ranging from logistics to home robotics. However, existing data
collection platforms are often proprietary, costly, or tailored to specific
robotic morphologies. We present OPEN TEACH, a new teleoperation system
leveraging VR headsets to immerse users in mixed reality for intuitive robot
control. Built on the affordable Meta Quest 3, which costs $500, OPEN TEACH
enables real-time control of various robots, including multi-fingered hands and
bimanual arms, through an easy-to-use app. Using natural hand gestures and
movements, users can manipulate robots at up to 90Hz with smooth visual
feedback and interface widgets offering closeup environment views. We
demonstrate the versatility of OPEN TEACH across 38 tasks on different robots.
A comprehensive user study indicates significant improvement in teleoperation
capability over the AnyTeleop framework. Further experiments exhibit that the
collected data is compatible with policy learning on 10 dexterous and
contact-rich manipulation tasks. Currently supporting Franka, xArm, Jaco, and
Allegro platforms, OPEN TEACH is fully open-sourced to promote broader
adoption. Videos are available at https://open-teach.github.io/.
