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The paper introduces TRIP-Bag, a portable teleoperation system housed in a suitcase, designed for rapid collection of high-fidelity robot manipulation data across diverse environments. It uses a puppeteer-style interface for direct joint-to-joint teleoperation, minimizing the embodiment gap between the data collection platform and the target robot. Experiments with non-expert users validated the system's usability, and benchmark manipulation policies trained on TRIP-Bag data demonstrated its practical value for robot learning.
Collect high-quality robot manipulation data anywhere with TRIP-Bag, a teleoperation system that fits in a suitcase and sets up in under 5 minutes.
Large scale, diverse demonstration data for manipulation tasks remains a major challenge in learning-based robot policies. Existing in-the-wild data collection approaches often rely on vision-based pose estimation of hand-held grippers or gloves, which introduces an embodiment gap between the collection platform and the target robot. Teleoperation systems eliminate the embodiment gap, but are typically impractical to deploy outside the laboratory environment. We propose TRIP-Bag (Teleoperation, Recording, Intelligence in a Portable Bag), a portable, puppeteer-style teleoperation system fully contained within a commercial suitcase, as a practical solution for collecting high-fidelity manipulation data across varied settings. With a setup time of under five minutes and direct joint-to-joint teleoperation, TRIP-Bag enables rapid and reliable data collection in any environment. We validated TRIP-Bag's usability through experiments with non-expert users, showing that the system is intuitive and easy to operate. Furthermore, we confirmed the quality of the collected data by training benchmark manipulation policies, demonstrating its value as a practical resource for robot learning.
