Explainable Human-Robot Training and Cooperation with Augmented Reality (2302.01039v1)

Abstract: The current spread of social and assistive robotics applications is increasingly highlighting the need for robots that can be easily taught and interacted with, even by users with no technical background. Still, it is often difficult to grasp what such robots know or to assess if a correct representation of the task is being formed. Augmented Reality (AR) has the potential to bridge this gap. We demonstrate three use cases where AR design elements enhance the explainability and efficiency of human-robot interaction: 1) a human teaching a robot some simple kitchen tasks by demonstration, 2) the robot showing its plan for solving novel tasks in AR to a human for validation, and 3) a robot communicating its intentions via AR while assisting people with limited mobility during daily activities.

Explainable Human-Robot Training and Cooperation with Augmented Reality

The paper entitled "Explainable Human-Robot Training and Cooperation with Augmented Reality" explores the integration of augmented reality (AR) in improving the teaching and interaction processes between humans and robots. As the deployment of social and assistive robots becomes more pervasive, addressing the challenge of making these systems accessible and comprehensible to non-technical users is paramount. The research presents a system architecture utilizing AR to enhance the transparency and perceived intelligence of robotic assistants, focusing on three primary use cases.

System Overview

The proposed system architecture consists of a combination of back-end and front-end components designed to foster seamless human-robot interactions. The back-end encompasses modules responsible for learning, planning, prediction, and motion generation, while the front-end involves AR interfaces that provide visualization and interaction capabilities. The system leverages the functionalities of the Microsoft HoloLens, which enables the overlaying of digital information onto the physical environment, thus granting users an intuitive interface to interact with robotic systems.

Use Case 1: Imitation Learning and Explainability

The first use case examines the process of human-robot interaction through imitation learning. The method empowers humans to teach robots new skills via demonstration. By employing semantic skill learning, the system captures symbolic representations encoding the preconditions, actions, and effects of demonstrated tasks. During demonstrations, AR displays assist the user by rendering visual cues that indicate objects and actions recognized by the robot. Furthermore, the system exhibits the capacity for making curious inquiries to validate or expand its knowledge base, thus accelerating the learning process and enhancing the user's understanding of what constitutes effective demonstrations.

Use Case 2: Robot Plan Visualization

The second use case investigates the utility of AR in visualizing robot-generated plans. Here, the system employs a symbolic planner to devise task solutions, combining previously learned skills. Before executing these plans, the robot presents a virtual simulation of the task using AR, allowing human users to observe, validate, or amend the proposed execution steps. This mechanism ensures that robot plans align with human expectations and preferences, fostering user trust and providing an opportunity for corrective feedback.

Use Case 3: Assisted Human-Robot Task Execution

In the third use case, the focus shifts to enhancing task execution in scenarios where robots assist humans with physical tasks. The system predicts human actions and movements, optimizes the ergonomic state of the human by suggesting alterations, and communicates these intentions through AR cues. This capability not only improves task performance but also mitigates potential ergonomic risks, thereby extending the applicability of robotic assistants to settings involving individuals with mobility constraints.

Implications and Future Work

The system architecture and use cases presented underscore the utility of AR in enhancing robot explainability and facilitating effective human-robot collaboration. The research suggests that AR, as a medium for revealing robot intentions and cognitive states, can significantly improve user experience and acceptance. The potential implications are especially pertinent in domains such as healthcare, where intuitive and reliable robotic assistance can substantially impact quality of life.

Future developments may focus on refining this AR-mediated interaction framework to cover a broader range of tasks and environments, potentially integrating more sophisticated machine learning models for better prediction and planning. Furthermore, addressing challenges related to user interface scalability and the adaptability of AR solutions in dynamic environments could advance the deployment of robust explainable robotic systems. As this technology evolves, it is positioned to make meaningful contributions within the interdisciplinary fields of robotics, artificial intelligence, and human-computer interaction.