A study on a Real-Time VR-Based Teleoperation Framework for Manipulator in Dynamic Environment

2026-05-29

AI Summary

This work addresses the challenges of conventional VR-based teleoperation in dynamically changing environments, where real-time responses to moving obstacles, workspace variations, and collision risks are critical—especially for novice operators. The authors propose a novel VR teleoperation framework that integrates GPU-accelerated inverse kinematics with trajectory optimization within each control cycle to generate collision-free joint commands that respect robotic motion constraints. This approach enables, for the first time in VR teleoperation, real-time adaptation to both static and dynamic obstacles while preserving operator intent and ensuring system robustness. Experimental results demonstrate stable performance across obstacle-free, static, and dynamic scenarios, accurately tracking user commands and producing safe detour trajectories suitable for deployment in real-world settings.

Abstract

Robot teleoperation enables safe, non-contact task execution in hazardous environments where direct human access is difficult, and its application has expanded with recent VR technologies. Many VR teleoperation studies, however, have primarily served as data-collection tools for robot imitation learning, so they often do not explicitly address dynamic obstacles, workspace changes, or collision risks during operation. For real deployment aimed at operator safety, teleoperation must react to dynamic situations with low latency and remain robust to mistakes made by inexperienced operators. This paper presents a VR teleoperation framework that supports real-time manipulation while handling collisions with both static and moving obstacles. The framework integrates GPU-accelerated inverse kinematics and trajectory optimization within a VR interface to generate feasible joint commands at each control cycle under robot constraints. Experiments with a 7-DoF manipulator demonstrate stable online behavior and collision-aware motion generation across three scenarios: obstacle-free, static-obstacle, and moving-obstacle environments. The results indicate that the proposed approach generates motion consistent with the operator's command while producing safe detours when obstacles interfere with the commanded path.

Problem

• teleoperation
• dynamic environment
• collision avoidance
• real-time control
• virtual reality

Innovation

• real-time teleoperation
• VR-based control
• collision avoidance
• GPU-accelerated inverse kinematics
• trajectory optimization