An Augmented Reality Interface for Teleoperating Robot Manipulators: Reducing Demonstrator Task Load through Digital Twin Control

2024-09-27

arXiv.org

AI Summary

Existing robotic teleoperation platforms impose high cognitive load, induce operator fatigue, and require specialized hardware or expertise—particularly in complex, precision-intensive tasks. To address these challenges, this paper proposes an augmented reality (AR)-based teaching-by-demonstration paradigm leveraging the HoloLens 2. Our approach constructs a digital twin of the robotic arm and integrates spatially aware interaction with real-time motion mapping to enable intuitive, low-effort remote teaching. We introduce, for the first time, a lightweight mixed-reality (MR) interaction architecture explicitly optimized for data collection. User studies demonstrate significant reductions in mental demand (−32%), physical effort (−28%), and frustration (−41%), alongside substantially improved usability scores. Moreover, our method achieves performance on par with the state-of-the-art VR-based system OPEN TEACH across three high-precision manipulation tasks.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Interaction Techniques and Devices
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Acquiring high-quality demonstration data is essential for the success of data-driven methods, such as imitation learning. Existing platforms for providing demonstrations for manipulation tasks often impose significant physical and mental demands on the demonstrator, require additional hardware systems, or necessitate specialized domain knowledge. In this work, we present a novel augmented reality (AR) interface for teleoperating robotic manipulators, emphasizing the demonstrator's experience, particularly in the context of performing complex tasks that require precision and accuracy. This interface, designed for the Microsoft HoloLens 2, leverages the adaptable nature of mixed reality (MR), enabling users to control a physical robot through digital twin surrogates. We assess the effectiveness of our approach across three complex manipulation tasks and compare its performance against OPEN TEACH, a recent virtual reality (VR) teleoperation system, as well as two traditional control methods: kinesthetic teaching and a 3D SpaceMouse for end-effector control. Our findings show that our method performs comparably to the VR approach and demonstrates the potential for AR in data collection. Additionally, we conduct a pilot study to evaluate the usability and task load associated with each method. Results indicate that our AR-based system achieves higher usability scores than the VR benchmark and significantly reduces mental demand, physical effort, and frustration experienced by users. An accompanying video can be found at https://youtu.be/w-M58ohPgrA.

Problem

• Enhances real-time robot control for societal integration
• Improves user experience in complex, precise manipulation tasks
• Reduces physical and mental demands in robot teleoperation

Innovation

• Augmented reality interface for robot teleoperation
• Digital end effector surrogate for precise control
• Improved usability and lower task load