Technology Category
Intelligent Robots: ManipulationMachine Learning: Imitation Learning & Inverse Reinforcement LearningHumans and AI: Interaction Techniques and Devices
Echo: An Open-Source, Low-Cost Teleoperation System with Force Feedback for Dataset Collection in Robot Learning
🤖 AI Summary
In robotic imitation learning, acquiring high-quality demonstration data remains costly, inefficient, and lacking in force feedback support. To address this, we propose an open-source, low-cost, force-feedback-enabled joint-space mapping teleoperation system specifically designed for UR manipulators and supporting both single- and dual-arm collaborative task demonstrations. Our key contributions are: (1) a novel lightweight adaptive-sensitivity force-feedback controller that jointly optimizes operational intuitiveness, trajectory accuracy, and demonstration reproducibility; (2) a modular architecture based on joint-space mapping and ROS integration, enabling rapid adaptation to diverse manipulators and humanoid robots; and (3) a custom force-controlled hand controller with a real-time synchronized data recording interface. Experiments demonstrate significant improvements in data collection efficiency and annotation quality. All hardware designs, assembly instructions, and software code are publicly released under an open-source license and have been widely adopted by the research community.
Technology Category
Application Category
📝 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/.
Problem
Research questions and friction points this paper is trying to address.
Enhancing dataset collection for manual and bimanual robot tasks
Providing precise force feedback teleoperation for UR manipulators
Simplifying imitation learning data collection with user-friendly tools
Innovation
Methods, ideas, or system contributions that make the work stand out.
Low-cost teleoperation system with force feedback
Custom controller with adjustable sensitivity modes
User-friendly dataset recording interface
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