This paper contributes to a taxonomy of augmented reality and robotics based on a survey of 460 research papers. Augmented and mixed reality (AR/MR) have emerged as a new way to enhance human-robot interaction (HRI) and robotic interfaces (e.g., actuated and shape-changing interfaces). Recently, an increasing number of studies in HCI, HRI, and robotics have demonstrated how AR enables better interactions between people and robots. However, often research remains focused on individual explorations and key design strategies, and research questions are rarely analyzed systematically. In this paper, we synthesize and categorize this research field in the following dimensions: 1) approaches to augmenting reality; 2) characteristics of robots; 3) purposes and benefits; 4) classification of presented information; 5) design components and strategies for visual augmentation; 6) interaction techniques and modalities; 7) application domains; and 8) evaluation strategies. We formulate key challenges and opportunities to guide and inform future research in AR and robotics.

pdf/augmented-reality-and-robotics-a-survey-and-taxonomy-for-ar-ssz3t2zw.pdf

Augmented Reality and Robotics: A Survey and Taxonomy for AR-enhanced Human-Robot Interaction and Robotic Interfaces

Biomechanics of the human lens and accommodative system: Functional relevance to physiological states.

In situ study of non-equilibrium solidification of CoCrFeNi high-entropy alloy and CrFeNi and CoCrNi ternary suballoys

Robots are becoming increasingly omnipresent in our daily lives, supporting us and carrying out autonomous tasks. In Human-Robot Interaction, human actors benefit from understanding the robot’s motion intent to avoid task failures and foster collaboration. Finding effective ways to communicate this intent to users has recently received increased research interest. However, no common language has been established to systematize robot motion intent. This work presents a scoping review aimed at unifying existing knowledge. Based on our analysis, we present an intent communication model that depicts the relationship between robot and human through different intent dimensions (intent type, intent information, intent location). We discuss these different intent dimensions and their interrelationships with different kinds of robots and human roles. Throughout our analysis, we classify the existing research literature along our intent communication model, allowing us to identify key patterns and possible directions for future research.

/pdf/how-to-communicate-robot-motion-intent-a-scoping-review-3ibz6wk3.pdf

How to Communicate Robot Motion Intent: A Scoping Review

Human-robot collaboration frequently requires extensive communication, e.g., using natural language and gesture. Augmented reality (AR) has provided an alternative way of bridging the communication gap between robots and people. However, most current AR-based human-robot communication methods are unidirectional, focusing on how the human adapts to robot behaviors, and are limited to single-robot domains. In this paper, we develop AR for Robots Collaborating with a Human (ARROCH), a novel algorithm and system that supports bidirectional, multi-turn, human-multi-robot communication in indoor multi-room environments. The human can see through obstacles to observe the robots’ current states and intentions, and provide feedback, while the robots’ behaviors are then adjusted toward human-multi-robot teamwork. Experiments have been conducted with real robots and human participants using collaborative delivery tasks. Results show that ARROCH outperformed a standard non-AR approach in both user experience and teamwork efficiency. In addition, we have developed a novel simulation environment using Unity (for AR and human simulation) and Gazebo (for robot simulation). Results in simulation demonstrate ARROCH’s superiority over AR-based baselines in human-robot collaboration.

ARROCH: Augmented Reality for Robots Collaborating with a Human

Augmented reality technology can enable robots to visualize their future actions giving users crucial information to avoid collisions and other conflicting actions. Although a robot's entire action plan could be visualized (such as the output of a navigational planner), how far into the future it is appropriate to display the robot's plan is unknown. We developed a dynamic path visualizer that projects the robot's motion intent at varying lengths depending on the complexity of the upcoming path. We tested our approach in a virtual game where participants were tasked to collect and deliver gems to a robot that moves randomly towards a grid of markers in a confined area. Preliminary results on a small sample size indicate no significant effect on task performance; however, open-ended responses reveal participants preference towards visuals that show longer path projections.

Dynamic Path Visualization for Human-Robot Collaboration

The ocular lens stiffens dramatically with age, resulting in a loss of function. However, the mechanism of stiffening remains unknown, at least in part due to difficulties in making reliable measurements of the intrinsic mechanical properties of the lens. Recent experiments have employed manual compression testing to evaluate the stiffness of murine lenses which have genotypes pertinent to human lens diseases. These experiments compare the extrinsic stiffness of lenses from the genotype of interest to the wild-type lens in an effort to reach conclusions regarding the cellular or molecular basis of lens stiffening. However, these comparisons are confounded by alterations in lens size and geometry which invariably accompany these genetic manipulations. Here, we utilize manual lens compression to characterize the stiffness of a porcine lens and a murine lens. An inverse elastographic technique was then developed to estimate the intrinsic shear modulus of each lens as well as the elastic modulus of the lens c...

https://www.worldscientific.com/doi/pdf/10.1142/S1793545817420093

Inverse elastographic method for analyzing the ocular lens compression test

Establishing common ground between an intelligent robot and a human requires communication of the robot's intention, behavior, and knowledge to the human to build trust and assure safety in a shared environment. This paper introduces SENSAR (Seeing Everything iN Situ with Augmented Reality), an augmented reality robotic system that enables robots to communicate their sensory and cognitive data in context over the real-world with rendered graphics, allowing a user to understand, correct, and validate the robot's perception of the world. Our system aims to support human-robot interaction research by establishing common ground where the perceptions of the human and the robot align.

SENSAR: A Visual Tool for Intelligent Robots for Collaborative Human-Robot Interaction.

We demonstrate TrainAR, an augmented reality (AR)-based tool that is designed to improve sim2real reinforcement learning (RL) for robots. Users with TRAinAR can tailor a virtual training environment with constraints to match the real-world, visualize training data to gain insights into an agent's learning process, and animate a robot's future actions before execution. The system described here enabled a robotic arm manipulator to learn how to navigate its end-effector toward a target object. The novelty of our AR application will hopefully enable users to better train a robot by quickly prototyping complex environments that are difficult to model.

https://dl.acm.org/doi/pdf/10.1145/3568294.3580207

Demonstrating TRAinAR: An Augmented Reality Tool that Helps Humans Teach Robots

This paper outlines the system design, capabilities and potential applications of an Augmented Reality (AR) framework developed for Robot Operating System (ROS) powered robots. The goal of this framework is to enable high-level human-robot collaboration and interaction. It allows the users to visualize the robot's state in intuitive modalities overlaid onto the real world and interact with AR objects as a means of communication with the robot. Thereby creating a shared environment in which humans and robots can interact and collaborate.

Creating a shared reality with robots

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