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Domain users (DUs) with a knowledge base in specialized fields are frequently excluded from authoring virtual reality (VR)-based applications in corresponding fields. This is largely due to the requirement of VR programming expertise needed to author these applications. To address this concern, we developed VRFromX, a system workflow design to make the virtual content creation process accessible to DUs irrespective of their programming skills and experience. VRFromX provides an in situ process of content creation in VR that (a) allows users to select regions of interest in scanned point clouds or sketch in mid-air using a brush tool to retrieve virtual models and (b) then attach behavioral properties to those objects. Using a welding use case, we performed a usability evaluation of VRFromX with 20 DUs from which 12 were novices in VR programming. Study results indicated positive user ratings for the system features with no significant differences across users with or without VR programming expertise. Based on the qualitative feedback, we also implemented two other use cases to demonstrate potential applications. We envision that the solution can facilitate the adoption of the immersive technology to create meaningful virtual environments.

 

Freehand gesture is an essential input modality for modern Augmented Reality (AR) user experiences. However, developing AR applications with customized hand interactions remains a challenge for end-users. Therefore, we propose GesturAR, an end-to-end authoring tool that supports users to create in-situ freehand AR applications through embodied demonstration and visual programming. During authoring, users can intuitively demonstrate the customized gesture inputs while referring to the spatial and temporal context. Based on the taxonomy of gestures in AR, we proposed a hand interaction model which maps the gesture inputs to the reactions of the AR contents. Thus, users can author comprehensive freehand applications using trigger-action visual programming and instantly experience the results in AR. Further, we demonstrate multiple application scenarios enabled by GesturAR, such as interactive virtual objects, robots, and avatars, room-level interactive AR spaces, embodied AR presentations, etc. Finally, we evaluate the performance and usability of GesturAR through a user study. 

There is an increasing trend of Virtual-Reality (VR) applications found in education, entertainment, and industry. Many of them utilize real world tools, environments, and interactions as bases for creation. However, creating such applications is tedious, fragmented, and involves expertise in authoring VR using programming and 3D-modelling softwares. This hinders VR adoption by decoupling subject matter experts from the actual process of authoring while increasing cost and time. We present VRFromX, an in-situ Do-It-Yourself (DIY) platform for content creation in VR that allows users to create interactive virtual experiences. Using our system, users can select region(s) of interest (ROI) in scanned point cloud or sketch in mid-air using a brush tool to retrieve virtual models and then attach behavioral properties to them. We ran an exploratory study to evaluate usability of VRFromX and the results demonstrate feasibility of the framework as an authoring tool. Finally, we implemented three possible use-cases to showcase potential applications. 

Modern manufacturing processes are in a state of flux, as they adapt to increasing demand for flexible and self-configuring production. This poses challenges for training workers to rapidly master new machine operations and processes, i.e. machine tasks. Conventional in-person training is effective but requires time and effort of experts for each worker trained and not scalable. Recorded tutorials, such as video-based or augmented reality (AR), permit more efficient scaling. However, unlike in-person tutoring, existing recorded tutorials lack the ability to adapt to workers’ diverse experiences and learning behaviors. We present AdapTutAR, an adaptive task tutoring system that enables experts to record machine task tutorials via embodied demonstration and train learners with different AR tutoring contents adapting to each user’s characteristics. The adaptation is achieved by continually monitoring learners’ tutorial-following status and adjusting the tutoring content on-the-fly and in-situ. The results of our user study evaluation have demonstrated that our adaptive system is more effective and preferable than the non-adaptive one. 

Recognition of human behavior plays an important role in context-aware applications. However, it is still a challenge for end-users to build personalized applications that accurately recognize their own activities. Therefore, we present CAPturAR, an in-situ programming tool that supports users to rapidly author context-aware applications by referring to their previous activities. We customize an AR head-mounted device with multiple camera systems that allow for non-intrusive capturing of user's daily activities. During authoring, we reconstruct the captured data in AR with an animated avatar and use virtual icons to represent the surrounding environment. With our visual programming interface, users create human-centered rules for the applications and experience them instantly in AR. We further demonstrate four use cases enabled by CAPturAR. Also, we verify the effectiveness of the AR-HMD and the authoring workflow with a system evaluation using our prototype. Moreover, we conduct a remote user study in an AR simulator to evaluate the usability. 

Machine tasks in workshops or factories are often a compound sequence of local, spatial, and body-coordinated human-machine interactions. Prior works have shown the merits of video-based and augmented reality (AR) tutoring systems for local tasks. However, due to the lack of a bodily representation of the tutor, they are not as effective for spatial and body-coordinated interactions. We propose avatars as an additional tutor representation to the existing AR instructions. In order to understand the design space of tutoring presence for machine tasks, we conduct a comparative study with 32 users. We aim to explore the strengths/limitations of the following four tutor options: video, non-avatar-AR, half-body+AR, and full-body+AR. The results show that users prefer the half-body+AR overall, especially for the spatial interactions. They have a preference for the full-body+AR for the body-coordinated interactions and the non-avatar-AR for the local interactions. We further discuss and summarize design recommendations and insights for future machine task tutoring systems. 

We present V.Ra, a visual and spatial programming system for robot-IoT task authoring. In V.Ra, programmable mobile robots serve as binding agents to link the stationary IoTs and perform collaborative tasks. We establish an ecosystem that coherently connects the three key elements of robot task planning , the human, robot and IoT, with one single mobile AR device. Users can perform task authoring with the Augmented Reality (AR) handheld interface, then placing the AR device onto the mobile robot directly transfers the task plan in a what-you-do-is-what-robot-does (WYDWRD) manner. The mobile device mediates the interactions between the user, robot, and the IoT oriented tasks, and guides the path planning execution with the embedded simultaneous localization and mapping (SLAM) capability. We demonstrate that V.Ra enables instant, robust and intuitive room-scale navigatory and interactive task authoring through various use cases and preliminary studies. 

We present V.Ra, a visual and spatial programming system for robot-IoT task authoring. In V.Ra, programmable mobile robots serve as binding agents to link the stationary IoTs and perform collaborative tasks. We establish an ecosystem that coherently connects the three key elements of robot task planning (human-robot-IoT) with one single AR-SLAM device. Users can perform task authoring in an analogous manner with the Augmented Reality (AR) interface. Then placing the device onto the mobile robot directly transfers the task plan in a what-you-do-is-what-robot-does (WYDWRD) manner. The mobile device mediates the interactions between the user, robot and IoT oriented tasks, and guides the path planning execution with the SLAM capability. 

We present GhostAR, a time-space editor for authoring and acting Human-Robot-Collaborative (HRC) tasks in-situ. Our system adopts an embodied authoring approach in Augmented Reality (AR), for spatially editing the actions and programming the robots through demonstrative role-playing. We propose a novel HRC workflow that externalizes user’s authoring as demonstrative and editable AR ghost, allowing for spatially situated visual referencing, realistic animated simulation, and collaborative action guidance. We develop a dynamic time warping (DTW) based collaboration model which takes the real-time captured motion as inputs, maps it to the previously authored human actions, and outputs the corresponding robot actions to achieve adaptive collaboration. We emphasize an in-situ authoring and rapid iterations of joint plans without an offline training process. Further, we demonstrate and evaluate the effectiveness of our workflow through HRC use cases and a three-session user study. 

Ani-Bot is a modular robotics system that allows users to control their DIY robots using Mixed-Reality Interaction (MRI). This system takes advantage of MRI to enable users to visually program the robot through the augmented view of a Head-Mounted Display (HMD). In this paper, we first explain the design of the Mixed-Reality (MR) ready modular robotics system, which allows users to instantly perform MRI once they finish assembling the robot. Then, we elaborate the augmentations provided by the MR system in the three primary phases of a construction kit's lifecycle: Creation, Tweaking, and Usage. Finally, we demonstrate Ani-Bot with four application examples and evaluate the system with a two-session user study. The results of our evaluation indicate that Ani-Bot does successfully embed MRI into the lifecycle (Creation, Tweaking, Usage) of DIY robotics and that it does show strong potential for delivering an enhanced user experience.