Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation

As artificial intelligence becomes an increasingly prevalent method of enhancing robotic capabilities, it is important to consider effective ways to train these learning pipelines and to leverage human expertise. Working towards these goals, a master-apprentice model is presented and is evaluated during a grasping task for effectiveness and human perception. The apprenticeship model augments self-supervised learning with learning by demonstration, efficiently using the human's time and expertise while facilitating future scalability to supervision of multiple robots; the human provides demonstrations via virtual reality when the robot cannot complete the task autonomously. Experimental results indicate that the robot learns a grasping task with the apprenticeship model faster than with a solely self-supervised approach and with fewer human interventions than a solely demonstration-based approach; 100% grasping success is obtained after 150 grasps with 19 demonstrations. Preliminary user studies evaluating workload, usability, and effectiveness of the system yield promising results for system scalability and deployability. They also suggest a tendency for users to overestimate the robot's skill and to generalize its capabilities, especially as learning improves.

Authors

• Joseph DelPreto
• Jeffrey I. Lipton
• Lindsay M. Sanneman
• Aidan J. Fay
• Christopher K. Fourie
• Changhyun Choi
• Daniela Rus

Keywords

• Robots
• Grasping
• Task analysis
• Three-dimensional displays
• Solid modeling
• Virtual reality
• Pipelines
• Virtually
• Robot Learning
• Master-apprentice Model
• Scalable
• Human Intervention
• Self-supervised Learning
• Multiple Robots
• Robot Capabilities
• Apprenticeship Model
• Neural Network
• Convolutional Neural Network
• Convolutional Layers
• Point Cloud
• Online Learning
• Subjective Ratings
• Depth Camera
• Block Type
• Human-robot Interaction
• Subject Pool
• Control Room
• Perception Of The Robot
• Actual Accuracy
• Types Of Robots