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Intelligent Mode-switching Framework for Teleoperation

Burak Kizilkaya, Changyang She, Guodong Zhao, Muhammad Ali Imran

TL;DR

The paper tackles the challenge of long-distance teleoperation by jointly optimizing mode-switching and communication constraints. It introduces an end-to-end framework that uses CNN-based user intention recognition on the operator side and a DRL-driven policy to switch between teleoperation and autonomous modes, supported by trajectory-level prediction for autonomous execution. Real-world data collection and extensive evaluation show that the approach can reduce communication load by up to 50% while maintaining or improving task completion probability, and demonstrate resilience to network impairments and novice operators. The work advances autonomous teleoperation by integrating perception, decision-making, and communication in a single design with practical performance gains for remote manipulation tasks.

Abstract

Teleoperation can be very difficult due to limited perception, high communication latency, and limited degrees of freedom (DoFs) at the operator side. Autonomous teleoperation is proposed to overcome this difficulty by predicting user intentions and performing some parts of the task autonomously to decrease the demand on the operator and increase the task completion rate. However, decision-making for mode-switching is generally assumed to be done by the operator, which brings an extra DoF to be controlled by the operator and introduces extra mental demand. On the other hand, the communication perspective is not investigated in the current literature, although communication imperfections and resource limitations are the main bottlenecks for teleoperation. In this study, we propose an intelligent mode-switching framework by jointly considering mode-switching and communication systems. User intention recognition is done at the operator side. Based on user intention recognition, a deep reinforcement learning (DRL) agent is trained and deployed at the operator side to seamlessly switch between autonomous and teleoperation modes. A real-world data set is collected from our teleoperation testbed to train both user intention recognition and DRL algorithms. Our results show that the proposed framework can achieve up to 50% communication load reduction with improved task completion probability.

Intelligent Mode-switching Framework for Teleoperation

TL;DR

The paper tackles the challenge of long-distance teleoperation by jointly optimizing mode-switching and communication constraints. It introduces an end-to-end framework that uses CNN-based user intention recognition on the operator side and a DRL-driven policy to switch between teleoperation and autonomous modes, supported by trajectory-level prediction for autonomous execution. Real-world data collection and extensive evaluation show that the approach can reduce communication load by up to 50% while maintaining or improving task completion probability, and demonstrate resilience to network impairments and novice operators. The work advances autonomous teleoperation by integrating perception, decision-making, and communication in a single design with practical performance gains for remote manipulation tasks.

Abstract

Teleoperation can be very difficult due to limited perception, high communication latency, and limited degrees of freedom (DoFs) at the operator side. Autonomous teleoperation is proposed to overcome this difficulty by predicting user intentions and performing some parts of the task autonomously to decrease the demand on the operator and increase the task completion rate. However, decision-making for mode-switching is generally assumed to be done by the operator, which brings an extra DoF to be controlled by the operator and introduces extra mental demand. On the other hand, the communication perspective is not investigated in the current literature, although communication imperfections and resource limitations are the main bottlenecks for teleoperation. In this study, we propose an intelligent mode-switching framework by jointly considering mode-switching and communication systems. User intention recognition is done at the operator side. Based on user intention recognition, a deep reinforcement learning (DRL) agent is trained and deployed at the operator side to seamlessly switch between autonomous and teleoperation modes. A real-world data set is collected from our teleoperation testbed to train both user intention recognition and DRL algorithms. Our results show that the proposed framework can achieve up to 50% communication load reduction with improved task completion probability.
Paper Structure (20 sections, 10 equations, 9 figures, 2 tables, 1 algorithm)

This paper contains 20 sections, 10 equations, 9 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Communication Load
  4. Task Completion Probability
  5. Problem Formulation
  6. Intelligent Mode-switching Framework
  7. Task-level Prediction: User Intention Recognition
  8. Trajectory-level Prediction
  9. DRL Framework
  10. States
  11. Actions
  12. Reward
  13. DRL Training
  14. Evaluation of the Proposed Framework
  15. Dataset Collection from Teleoperation Prototype
  16. ...and 5 more sections

Figures (9)

  • Figure 1: Intelligent Mode Switching Framework
  • Figure 2: Dataset collection from teleoperation testbed
  • Figure 3: User intention recognition (task-level prediction) accuracy vs observation length (% of task).
  • Figure 4: DRL training results for task completion probability, where $\psi_m = 0.85$.
  • Figure 5: Task example.
  • ...and 4 more figures