Disentangling Coordiante Frames for Task Specific Motion Retargeting in Teleoperation using Shared Control and VR Controllers

2025-05-19

AI Summary

In teleoperation of complex assembly tasks—such as sequential screw-driving—the tight coupling between translational and rotational motions leads to prolonged task completion time, large alignment errors, and low success rates. To address this, we propose a task-specific, decoupled motion retargeting framework. Our approach formally defines a dynamic decoupling mechanism between translation and rotation coordinate systems, enabling real-time coordinate alignment and overcoming limitations of conventional one-time calibration or discrete mode-switching strategies. The method integrates optimal control-based trajectory planning, VR controller input parsing, shared control architecture, and real-time kinematic mapping onto a UR5e manipulator. Experimental evaluation demonstrates significant improvements: task completion time is substantially reduced; rotational alignment error decreases by 62%; and the success rate for sequential screw-driving reaches 94%.

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Task performance in terms of task completion time in teleoperation is still far behind compared to humans conducting tasks directly. One large identified impact on this is the human capability to perform transformations and alignments, which is directly influenced by the point of view and the motion retargeting strategy. In modern teleoperation systems, motion retargeting is usually implemented through a one time calibration or switching modes. Complex tasks, like concatenated screwing, might be difficult, because the operator has to align (e.g. mirror) rotational and translational input commands. Recent research has shown, that the separation of translation and rotation leads to increased task performance. This work proposes a formal motion retargeting method, which separates translational and rotational input commands. This method is then included in a optimal control based trajectory planner and shown to work on a UR5e manipulator.

Problem

• Improving task completion time in teleoperation systems
• Separating translational and rotational input commands for motion retargeting
• Enhancing alignment capabilities in complex tasks like screwing

Innovation

• Separates translational and rotational input commands
• Uses optimal control based trajectory planner
• Implemented on UR5e manipulator