Purdue Research Foundation

Yuanzhi Cao

Karthik Ramani

Tianyi Wang

Zhuangying Xu

Ke Huo

Xun Qian

Gaoping Huang

Alexander J Quinn

A machine task tutorial system is disclosed that utilizes augmented reality to enable an expert user to record a tutorial for a machine task that can be learned by different trainee users in an adaptive manner. The machine task tutorial system advantageously utilizes an adaptation model that focuses on spatial and bodily visual presence for machine task tutoring. The machine task tutorial system advantageously enables adaptive tutoring in the recorded-tutorial environment based on machine state and user activity recognition. The machine task tutorial system advantageously utilizes AR to provide tutorial recording, adaptive visualization, and state recognition. In this way, the machine task tutorial system supports more effective apprenticeship and training for machine tasks in workshops or factories.

Adaptive tutoring system for machine tasks in augmented reality

Disclosed is a visual and spatial programming system for robot navigation and robot-IoT task authoring. Programmable mobile robots serve as binding agents to link stationary IoT devices and perform collaborative tasks. Three key elements of robot task planning (human-robot-IoT) are coherently connected with one single smartphone device. Users can perform visual task authoring in an analogous manner to the real tasks that they would like the robot to perform with using an augmented reality interface. The mobile device mediates interactions between the user, robot(s), and IoT device-oriented tasks, guiding the path planning execution with Simultaneous Localization and Mapping (SLAM) to enable robust room-scale navigation and interactive task authoring.

Augmented reality interface for authoring tasks for execution by a programmable robot

A system and method for authoring and performing Human-Robot-Collaborative (HRC) tasks is disclosed. The system and method adopt an embodied authoring approach in Augmented Reality (AR), for spatially editing the actions and programming the robots through demonstrative role-playing. The system and method utilize an intuitive 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. The system and method utilize 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.

System and method for embodied authoring of human-robot collaborative tasks with augmented reality

Robot navigation and robot-IoT interactive task planning using augmented reality

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West Lafayette, IN, United States of America

Yuanzhi Cao