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OPEN TEACH: A Versatile Teleoperation System for Robotic Manipulation (2403.07870v1)

Published 12 Mar 2024 in cs.RO

Abstract: 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/.

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Citations (23)
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Summary

  • The paper presents a unified teleoperation framework that supports diverse robot types with plug-and-play, calibration-free VR control.
  • It integrates a cost-effective VR system delivering high-frequency (90Hz) visual feedback, achieving an average policy success rate of 86%.
  • User studies show that even novices can efficiently operate varied robotic systems, simplifying teleoperation data collection and collaboration.

Unveiling \method{}: A Unified Teleoperation Framework for Robotic Manipulation Across Varied Morphologies

Introduction to \method{}

The domain of robotic manipulation has witnessed substantial progression with the introduction of data-driven learning techniques, fostering enhancements in multi-fingered dexterity and bimanual manipulation. An emerging challenge within this field is the creation of versatile and accessible platforms for teleoperation data collection that can adapt across diverse robot morphologies without necessitating hefty investments or specialized setups. Addressing this, we introduce \method{}, a novel teleoperation system embedding VR technology to offer a seamless, calibration-free teleoperation experience over a broad spectrum of robotic configurations including single and bimanual arms, mobile manipulators, and multi-fingered hands.

Key Features and Capabilities

\method{} is distinguished by its plug-and-play nature, extensive compatibility across robot types, and the implementation of a cost-effective VR headset for an immersive teleoperation experience. Here are its core attributes:

  • Unified Teleoperation Across Robots: Unlike existing systems that are limited by cost, propriety, or specificity to robot types, \method{} introduces a highly versatile teleoperation framework. It demonstrates compatibility with a range of robots like Franka, xArm, Jaco for arms, and Allegro and Hello Stretch for hands and mobile manipulation.
  • High-Frequency Control and Visual Feedback: Leveraging the Meta Quest 3 VR headset, \method{} enables users to teleoperate robots with up to 90Hz of visual feedback. This supports nuanced control and instant correction capabilities essential for executing intricate and long-horizon tasks effectively.
  • User-Friendly and Calibration-Free Setup: Engineered for ease of use, \method{}'s setup bypasses the need for labor-intensive calibration processes, providing a straightforward pathway for users to engage in robot teleoperation.
  • Open-Source Contribution: In line with fostering community growth and collaboration, the entirety of \method{}—spanning the VR application, interface, and robot controllers— is made available open-source. We invite contributions to expand its applicability further.

Experimental Validation and Impact

A series of experiments involving 38 tasks across variations of robots underline \method{}'s versatility. This system not only enables users to perform a wide array of tasks but also facilitates the collection of high-quality data, demonstrating an average policy success rate of 86\% across tasks. Such achievements mark a significant stride towards simplifying data collection for robot learning applications.

Furthermore, a comprehensive user paper underscores \method{}'s intuitive design. Participants, including those with no prior teleoperation experience, were able to effectively control robots and complete designated tasks, showcasing the system's ease-of-use and potential to reduce training time for teleoperators.

Future Directions and Community Invitation

While \method{} marks a notable advancement in robotic teleoperation, it is not devoid of limitations. The precision of control is contingent upon the accuracy of the VR headset's hand pose estimation, which can sometimes be compromised due to occlusions. Future work aimed at refining hand tracking technologies could further elevate the performance and user experience of teleoperation systems like \method{}.

We envision \method{} as a foundation for more accessible robot teleoperation and encourage the community to engage with our open-source repository. Whether through developing interfaces for additional robots, enhancing the VR experience, or integrating advanced control strategies, there is ample room for extending \method{}'s capabilities. Contributions are warmly welcomed, and we are committed to supporting their integration and dissemination within the broader robotics and AI community.

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