TeleHairing: A Teleoperation Baseline for Robotic Haircutting

2026-10-05

AI Summary

This study addresses the absence of closed-loop benchmarks encompassing communication, visual feedback, and interruption handling in robotic hairdressing teleoperation by constructing a closed-loop teleoperation architecture evaluated on mannequin testbeds. Through multimodal network latency measurements and trajectory error analysis, this work quantifies, for the first time, the delay distribution characteristics across local, relayed, and remote deployments. Furthermore, it proposes a detection-loss rebasing algorithm to optimize interruption recovery mechanisms. The research identifies the primary sources of latency in remote mode and reveals that end-effector retraction phases dominate trajectory errors; upon excluding these segments, the root mean square error decreases to 9.6 mm. Additionally, the system’s capability for smooth motion recovery is validated, thereby establishing a systematic evaluation framework for hairdressing teleoperation.

Abstract

Robotic haircutting requires controlled tool motion near the head while simultaneously accounting for communication, visual feedback, tool actuation, and interruption handling. Existing studies still lack an operator-in-the-loop reference for analyzing these coupled behaviors before human trials or stronger autonomy. This paper presents TeleHairing, a closed-loop teleoperation architecture for mannequin-based robotic haircutting evaluation under local, relay, and remote deployment conditions. Logged timing shows that the main remote latency increase occurs before the robot-side control endpoint: overall timing reached 190.5~ms in remote mode, while robot-side command queue, control processing, and control-to-robot timing remained similar across modes. Trajectory analysis shows that the larger remote command-following error was dominated by the terminal withdrawal segment rather than accumulated uniformly over the path; excluding this segment reduced remote root-mean-square error (RMSE) from 45.1 mm to 9.6 mm. Detection-loss trials further show that rebase events resumed motion without a large target jump under the tested condition. These results clarify how deployment, execution, and interruption affect the robotic haircutting teleoperation loop, providing a quantitative reference for future autonomy, safety, and user-facing studies.

Problem

• Robotic haircutting
• Teleoperation
• Latency analysis
• Trajectory error
• Operator-in-the-loop

Innovation

• teleoperation
• robotic haircutting
• closed-loop architecture
• latency analysis
• trajectory error