Summary
Construction robots need to work well in dangerous and messy environments where humans are good at adapting but robots are good at being precise. The authors developed a system that lets human operators switch smoothly between fully controlling the robot through virtual reality, sharing control with the robot, and letting the robot work on its own for repetitive tasks. This approach helps humans guide the robot when things get tricky but lets the robot handle routine, precise actions to save effort. Tests showed that this teamwork reduces workload and improves the robot’s ability to handle unexpected problems during building work.
What this means in practice
- •For construction robot operators: Control dual-arm construction robots with a system that lets operators switch between VR teleoperation, shared control, and autonomy to improve handling of unpredictable conditions.
- •For industrial automation engineers: Design control systems that blend human supervision and robot autonomy for tasks requiring precision and adaptability in unstructured environments.
Authors
Jinhua Chen, Xiaoji Xu, Zhongyuan Liao, Huang Zhenmin, Zhang Ye, Changying Xiang
Abstract
Construction tasks in hazardous and unstructured environments require both human adaptability and robotic precision, yet existing approaches often treat teleoperation and autonomy as separate control modes. This paper presents a human-in-the-loop control paradigm that enables structured transitions among VR-guided teleoperation, semi-autonomous shared control, and rule-based autonomous execution for contact-rich dual-arm construction tasks. The proposed system combines immersive VR guidance, a manually defined library of reusable skill primitives, and a shared-autonomy framework with explicit human-robot role allocation and command-priority rules. This integration allows operators to retain supervisory and corrective authority while delegating repetitive and precision-critical actions to the robot. The paradigm is validated on a dual-arm platform through representative facade-installation tasks. Results show that autonomous execution provides the best performance under well-calibrated nominal conditions, whereas semi-autonomous control improves resilience when environmental disturbances exceed the predefined autonomous pipeline's correction capability. Semi-autonomous control also substantially reduces subjective operator workload compared with full teleoperation. These findings demonstrate that effective construction-robot control should adapt human involvement to task and environmental uncertainty, using autonomy for repeatable operations and shared control for disturbance recovery.