TeachBot: Towards teaching robotics fundamentals for human-robot collaboration at work

Area of Science:

• Robotics and Automation
• Human-Computer Interaction
• Workforce Development

Background:

• Manufacturing industries face challenges due to a shortage of skilled workers capable of operating robots.
• Existing training methods may not fully engage learners or effectively impart complex robotic concepts.

Purpose of the Study:

• To introduce TeachBot, a novel robotic training system utilizing a
• Learning by Touching
• methodology.
• To evaluate the effectiveness of this interactive training approach compared to traditional observation-based methods.

Main Methods:

• Development of TeachBot, a robotic instructor delivering interactive lectures with graphical and physical demonstrations.
• Implementation of a human-computer interface integrating auditory, visual, and haptic feedback.
• A pilot randomized controlled test involving adult learners to compare "Learning by Touching" with passive observation.

Main Results:

• Suggestive evidence indicates that "Learning by Touching" enhances learning effectiveness in a robotic context for adult learners.
• Learners who touched the robot demonstrated improved ability to integrate knowledge about robotics.
• The "touching" group showed statistically significant gains in self-efficacy and trending gains in knowledge of robotic concepts.

Conclusions:

• The TeachBot system and its "Learning by Touching" methodology offer a promising approach to engaging and effective robotics training.
• Interactive, embodied learning experiences are crucial for knowledge integration and building self-efficacy in new technologies.
• This approach has the potential to address the skilled worker shortage in manufacturing by improving robotics education.