🤖 AI Summary
Existing teleoperation methods suffer from strong hardware dependencies and mismatched human–robot control frequencies, limiting their applicability in hazardous environment operations and robot learning demonstration collection. To address these challenges, this paper proposes a cross-platform plug-and-play teleoperation framework. Our approach introduces: (i) a universal hardware interface automatically derived from URDF models; (ii) an online continuous trajectory generation algorithm that operates without low-level closed-loop access; (iii) minimum-stretch spline optimization for enhanced motion smoothness; and (iv) dynamic switching between precision-priority and velocity-priority control modes. Implemented with a C++ core and Python API, the system demonstrates broad generalizability, real-time performance, and smooth control across multiple single- and dual-arm robotic platforms. It achieves seamless mapping from low-frequency human inputs to high-frequency robot execution, significantly improving operational flexibility and deployment efficiency.
📝 Abstract
Teleoperation is crucial for hazardous environment operations and serves as a key tool for collecting expert demonstrations in robot learning. However, existing methods face robotic hardware dependency and control frequency mismatches between teleoperation devices and robotic platforms. Our approach automatically extracts kinematic parameters from unified robot description format (URDF) files, and enables pluggable deployment across diverse robots through uniform interfaces. The proposed interpolation algorithm bridges the frequency gap between low-rate human inputs and high-frequency robotic control commands through online continuous trajectory generation,
{while requiring no access to the closed, bottom-level control loop}. To enhance trajectory smoothness, we introduce a minimum-stretch spline that optimizes the motion quality. The system further provides precision and rapid modes to accommodate different task requirements. Experiments across various robotic platforms including dual-arm ones demonstrate generality and smooth operation performance of our methods. The code is developed in C++ with python interface, and available at https://github.com/IRMV-Manipulation-Group/UTTG.