A Single Scale Doesn't Fit All: Adaptive Motion Scaling for Efficient and Precise Teleoperation

2025-03-03

AI Summary

To address the frequent clutching, reduced positioning accuracy, and excessive cognitive load caused by workspace scale mismatch in teleoperation, this paper proposes a dynamic motion-scaling shared control method based on human motion intention recognition. The approach innovatively decouples the operator’s arm trajectory into coarse- and fine-motion modalities and integrates fuzzy C-means probabilistic classification with an online model update mechanism to enable bidirectional, adaptive scaling between human and robot. The method encompasses motion-intention feature extraction, real-time trajectory analysis, and user-feedback-driven online parameter optimization. Experimental evaluation using a peg-transfer task demonstrates a 38.46% reduction in clutching events, an 11.96% decrease in task completion time, and a 58.01% reduction in NASA-TLX cognitive workload. These results confirm significant improvements in teleoperation efficiency, positioning accuracy, and naturalness of human–robot collaboration.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Motion & Tracking

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: The sharing economy
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Teleoperation is increasingly employed in environments where direct human access is difficult, such as hazardous exploration or surgical field. However, if the motion scale factor(MSF) intended to compensate for workspace-size differences is set inappropriately, repeated clutching operations and reduced precision can significantly raise cognitive load. This paper presents a shared controller that dynamically applies the MSF based on the user's intended motion scale. Inspired by human motor skills, the leader arm trajectory is divided into coarse(fast, large-range movements) and fine(precise, small-range movements), with three features extracted to train a fuzzy C-means(FCM) clustering model that probabilistically classifies the user's motion scale. Scaling the robot's motion accordingly reduces unnecessary repetition for large-scale movements and enables more precise control for fine operations. Incorporating recent trajectory data into model updates and offering user feedback for adjusting the MSF range and response speed allows mutual adaptation between user and system. In peg transfer experiments, compared to using a fixed single scale, the proposed approach demonstrated improved task efficiency(number of clutching and task completion time decreased 38.46% and 11.96% respectively), while NASA-TLX scores confirmed a meaningful reduction(58.01% decreased) in cognitive load. This outcome suggests that a user-intent-based motion scale adjustment can effectively enhance both efficiency and precision in teleoperation.

Problem

• Adaptive motion scaling for efficient teleoperation
• Reducing cognitive load in teleoperation tasks
• Enhancing precision and efficiency in robot control

Innovation

• Dynamic motion scale factor adjustment
• Fuzzy C-means clustering for motion classification
• User feedback for mutual adaptation