ACM SIGGRAPH 2019 Posters

Learning from Human-Robot Interactions in Modeled Scenes

Mark Murnane, Max Breitmeyer, Francis Ferraro, and Cynthia Matuszek

July 28, 2019

There is increasing interest in using robots in simulation to understand and improve human-robot interaction (HRI). At the same time, the use of simulated settings to gather training data promises to help address a major data bottleneck in allowing robots to take advantage of powerful machine learning approaches. In this paper, we describe a prototype system that combines the robot operating system (ROS), the simulator Gazebo, and the Unity game engine to create human-robot interaction scenarios. A person can engage with the scenario using a monitor wall, allowing simultaneous collection of realistic sensor data and traces of human actions.

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Tags: robotics

Type: InProceedings

Publisher: ACM

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