Accelerating imitation learning through crowdsourcing

Although imitation learning is a powerful technique for robot learning and knowledge acquisition from näıve human users, it often suffers from the need for expensive human demonstrations. In some cases the robot has an insufficient number of useful demonstrations, while in others its learning ability is limited by the number of users it directly interacts with. We propose an approach that overcomes these shortcomings by using crowdsourcing to collect a wider variety of examples from a large pool of human demonstrators online. We present a new goal-based imitation learning framework which utilizes crowdsourcing as a major source of human demonstration data. We demonstrate the effectiveness of our approach experimentally on a scenario where the robot learns to build 2D object models on a table from basic building blocks using knowledge gained from locals and online crowd workers. In addition, we show how the robot can use this knowledge to support human-robot collaboration tasks such as goal inference through object-part classification and missing-part prediction. We report results from a user study involving fourteen local demonstrators and hundreds of crowd workers on 16 different model building tasks.

Authors

• Michael Jae-Yoon Chung
• Maxwell Forbes
• Maya Cakmak
• Rajesh P. N. Rao

Keywords

• Robots
• Crowdsourcing
• Data models
• Buildings
• Data collection
• Graphical models
• Computational modeling
• Imitation Learning
• User Study
• Knowledge Acquisition
• Variety Of Examples
• Robot Learning
• Human-robot Collaboration
• Number Of Demonstrations
• Actuator
• Scoring Function
• Task Difficulty
• Combined Score
• Model In Step
• Graphical Model
• Marginal Distribution
• Telework
• Mechanical Turk
• Class Assignment
• Object Parts
• Crowdsourced Data
• Set Of Names
• Model Name