TRIP-Bag: A Portable Teleoperation System for Plug-and-Play Robotic Arms and Leaders
This provides a practical solution for researchers and practitioners in robotics to efficiently gather demonstration data outside laboratory settings, though it is incremental as it builds on existing teleoperation concepts.
The paper tackles the challenge of collecting diverse, high-fidelity manipulation data for robot learning by introducing TRIP-Bag, a portable teleoperation system that reduces setup time to under five minutes and enables intuitive operation by non-experts, leading to successful training of benchmark policies.
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.