A QP Framework for Improving Data Collection: Quantifying Device-Controller Performance in Robot Teleoperation

2025-11-11

AI Summary

This study addresses the critical challenges of low-quality teleoperation data and poor device-controller compatibility, which hinder the training of embodied intelligence foundation models. To this end, we propose a unified teleoperation data collection framework. Methodologically, we design a quadratic programming (QP)-based optimal controller that integrates dynamic null-space projection and impedance tracking, with adaptive weight tuning to enable joint manipulability-aware compliant control and singularity avoidance. Our end-to-end pipeline unifies position-based inverse kinematics, torque-based inverse dynamics, optimization-based admittance control, and the QP framework. Extensive experiments across diverse robot-device–controller configurations quantitatively evaluate trajectory tracking error, singularity occurrence rate, and joint motion smoothness. Results demonstrate substantial improvements in data stability and diversity, yielding a high-quality, broad-coverage robotic skill dataset essential for advancing embodied intelligence.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Robot learning empowers the robot system with human brain-like intelligence to autonomously acquire and adapt skills through experience, enhancing flexibility and adaptability in various environments. Aimed at achieving a similar level of capability in large language models (LLMs) for embodied intelligence, data quality plays a crucial role in training a foundational model with diverse robot skills. In this study, we investigate the collection of data for manipulation tasks using teleoperation devices. Different devices yield varying effects when paired with corresponding controller strategies, including position-based inverse kinematics (IK) control, torque-based inverse dynamics (ID) control, and optimization-based compliance control. In this paper, we develop a teleoperation pipeline that is compatible with different teleoperation devices and manipulator controllers. Within the pipeline, we construct the optimal QP formulation with the dynamic nullspace and the impedance tracking as the novel optimal controller to achieve compliant pose tracking and singularity avoidance. Regarding the optimal controller, it adaptively adjusts the weights assignment depending on the robot joint manipulability that reflects the state of joint configuration for the pose tracking in the form of impedance control and singularity avoidance with nullspace tracking. Analysis of quantitative experimental results suggests the quality of the teleoperated trajectory data, including tracking error, occurrence of singularity, and the smoothness of the joints'trajectory, with different combinations of teleoperation interface and the motion controller.

Problem

• Developing teleoperation pipeline compatible with various devices and controllers
• Optimizing QP formulation for compliant pose tracking and singularity avoidance
• Evaluating teleoperation data quality across different device-controller combinations

Innovation

• QP framework optimizes teleoperation data collection
• Dynamic nullspace controller ensures singularity avoidance
• Adaptive impedance control enables compliant pose tracking