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

Yuxuan Zhao, Yuanchen Tang, Jindi Zhang, Hongyu Yu

TL;DR

The paper tackles data quality in teleoperation-driven robot manipulation by linking device choice and controller design to data quality metrics. It introduces a QP-based compliance controller with impedance tracking and a manipulability-driven dynamic null-space mechanism, using $M_i=\\

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.

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

TL;DR

The paper tackles data quality in teleoperation-driven robot manipulation by linking device choice and controller design to data quality metrics. It introduces a QP-based compliance controller with impedance tracking and a manipulability-driven dynamic null-space mechanism, using $M_i=\\

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.

Paper Structure

This paper contains 22 sections, 19 equations, 23 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Teleoperation Device
  4. QP-based Controller Framework
  5. Singularity Avoidance Method
  6. Mechanical Structure
  7. Robotic Manipulator Redesign
  8. Forward Kinematics
  9. Controller Design
  10. Prelinminaries
  11. Position- and Torque-based Controller
  12. QP Compliance Controller
  13. Dynamic Nullspace Tracking Adaptation
  14. Experiment
  15. Experiment Setup and Implementation
  16. ...and 7 more sections

Figures (23)

  • Figure 1: Framework of the teleoperation process for data collection
  • Figure 2: Descriptions of the mechanical modifications for Unitree H1 humanoid robot.
  • Figure 3: Schematic Diagram of Screw-Theoretic Modeling for the Unitree H1 Robot.
  • Figure 4: Rokoko Suit integrated with Rokoko Gloves
  • Figure 5: Camera integrated with WilorR
  • ...and 18 more figures