Impact of embodied training on object recognition

The ability to perform robust, precise, real-time visual recognition is extremely critical for the use of robotic systems in real-world applications. This paper explores the use of Convolution Neural Networks (CNN) and human assisted training in teaching a robot to recognize novel objects. We investigated the impact of providing instructions to a human teacher during a training scenario for novel objects. Participants in the naïve condition were provided verbal instructions by the robot, and participants in the embodied condition were provided embodied demonstrations by the robot. The results showed that a vision system trained by participants with embodied instructions clearly outperformed a system trained by naïve participants. The latest computer vision techniques combined with human assisted teaching was found to provide excellent results for novel object recognition.