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Echo: An Open-Source, Low-Cost Teleoperation System with Force Feedback for Dataset Collection in Robot Learning

Artem Bazhenov, Sergei Satsevich, Sergei Egorov, Farit Khabibullin, Dzmitry Tsetserukou

TL;DR

Echo addresses the data bottleneck in robot learning by providing a low-cost, open-source teleoperation system with force feedback and adjustable sensitivity, designed for joint-matching control of a UR manipulator. It integrates a modular five-board electronics stack, a TPU-based force-feedback joystick, and a Robotiq fingertip with an RP-C7.6-LT sensor to enable precise, intuitive manipulation and high-quality training data collection for imitation learning. The authors demonstrate advantages in complex, high-precision tasks and show improved data collection efficiency compared with vision-based mocap systems, while maintaining manufacturability for small-series production. By open-sourcing hardware and software and offering thorough fabrication instructions, Echo lowers barriers to entry for labs and startups pursuing robot learning through data-driven methods.

Abstract

In this article, we propose Echo, a novel joint-matching teleoperation system designed to enhance the collection of datasets for manual and bimanual tasks. Our system is specifically tailored for controlling the UR manipulator and features a custom controller with force feedback and adjustable sensitivity modes, enabling precise and intuitive operation. Additionally, Echo integrates a user-friendly dataset recording interface, simplifying the process of collecting high-quality training data for imitation learning. The system is designed to be reliable, cost-effective, and easily reproducible, making it an accessible tool for researchers, laboratories, and startups passionate about advancing robotics through imitation learning. Although the current implementation focuses on the UR manipulator, Echo architecture is reconfigurable and can be adapted to other manipulators and humanoid systems. We demonstrate the effectiveness of Echo through a series of experiments, showcasing its ability to perform complex bimanual tasks and its potential to accelerate research in the field. We provide assembly instructions, a hardware description, and code at https://eterwait.github.io/Echo/.

Echo: An Open-Source, Low-Cost Teleoperation System with Force Feedback for Dataset Collection in Robot Learning

TL;DR

Echo addresses the data bottleneck in robot learning by providing a low-cost, open-source teleoperation system with force feedback and adjustable sensitivity, designed for joint-matching control of a UR manipulator. It integrates a modular five-board electronics stack, a TPU-based force-feedback joystick, and a Robotiq fingertip with an RP-C7.6-LT sensor to enable precise, intuitive manipulation and high-quality training data collection for imitation learning. The authors demonstrate advantages in complex, high-precision tasks and show improved data collection efficiency compared with vision-based mocap systems, while maintaining manufacturability for small-series production. By open-sourcing hardware and software and offering thorough fabrication instructions, Echo lowers barriers to entry for labs and startups pursuing robot learning through data-driven methods.

Abstract

In this article, we propose Echo, a novel joint-matching teleoperation system designed to enhance the collection of datasets for manual and bimanual tasks. Our system is specifically tailored for controlling the UR manipulator and features a custom controller with force feedback and adjustable sensitivity modes, enabling precise and intuitive operation. Additionally, Echo integrates a user-friendly dataset recording interface, simplifying the process of collecting high-quality training data for imitation learning. The system is designed to be reliable, cost-effective, and easily reproducible, making it an accessible tool for researchers, laboratories, and startups passionate about advancing robotics through imitation learning. Although the current implementation focuses on the UR manipulator, Echo architecture is reconfigurable and can be adapted to other manipulators and humanoid systems. We demonstrate the effectiveness of Echo through a series of experiments, showcasing its ability to perform complex bimanual tasks and its potential to accelerate research in the field. We provide assembly instructions, a hardware description, and code at https://eterwait.github.io/Echo/.

Paper Structure

This paper contains 14 sections, 1 equation, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Data Acquisition Classification
  4. Comparison of Teleoperation Systems
  5. System Architecture
  6. Joints Design
  7. Joystick Design and Force-Feedback Working Principle
  8. Robotic finger
  9. Electronics
  10. Force Sensor Linearization System
  11. Experiments
  12. Participant Recruitment and Entry Threshold
  13. Participants' Results
  14. Conclusion

Figures (9)

  • Figure 2: Joint scheme: PCB with a potentiometer and a connector (1), bracket (2), metal rod (3), bearing (4), flexible element (5), shaft holder (6), screw-based limiter (7)
  • Figure 3: Joystick scheme: handle (1), actuation stick (2), trigger sleeve (3), bearing (4), motor (5), flexible element (6), metal rod (7), PCB (8), metal rod (9), bracket (10)
  • Figure 4: Schematic of the robotic finger mechanism
  • Figure 5: System architecture
  • Figure 6: Potentiometer PCB
  • ...and 4 more figures