TRIP-Bag: A Portable Teleoperation System for Plug-and-Play Robotic Arms and Leaders

TL;DR

TRIP-Bag is a portable teleoperation system in a suitcase that enables quick, high-quality data collection for robot manipulation tasks across diverse environments, facilitating learning-based robot policy development.

Contribution

We introduce TRIP-Bag, a portable, plug-and-play teleoperation system that simplifies data collection for manipulation tasks outside laboratory settings.

Findings

TRIP-Bag can be set up in under five minutes.

Non-expert users find TRIP-Bag intuitive and easy to operate.

Data collected with TRIP-Bag effectively trains benchmark manipulation policies.

Abstract

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.