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

Noboru Myers, Sankalp Yamsani, Obin Kwon, Joohyung Kim

TL;DR

TRIP-Bag is proposed, 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 and validated its usability through experiments with non-expert users, demonstrating its value as a practical resource for robot learning.

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.

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

TL;DR

TRIP-Bag is proposed, 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 and validated its usability through experiments with non-expert users, demonstrating its value as a practical resource for robot learning.

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.
Paper Structure (17 sections, 10 figures, 2 tables)

This paper contains 17 sections, 10 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. System Design
  4. Hardware
  5. Software
  6. Data Collection
  7. Setup
  8. Data Collection Process
  9. Experiments
  10. Target Tasks
  11. Task 1: Fruit Collecting
  12. Task 2: Egg Cracking
  13. Portability
  14. Usability
  15. Data Diversity
  16. ...and 2 more sections

Figures (10)

  • Figure 1: The setup process of collecting data with our system. A few of the diverse locations we collected in.
  • Figure 2: The breakdown of the hardware components and view of the cameras.
  • Figure 3: Workspace of TRIP-Bag. All dimensions are in mm.
  • Figure 4: Overview of the software architecture, including device integration with the leader and follower PCs.
  • Figure 5: Range of deployment locations where the bag has been used for data collection and non-expert operation.
  • ...and 5 more figures