Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation
By Joseph DelPreto, J. Lipton, Lindsay M. Sanneman, Aidan J. Fay, Christopher K. Fourie, Changhyun Choi, Daniela Rus. 2020
43 citations recorded by the source corpus.
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.
Subfields: Biomedical Engineering, Control and Systems Engineering, Mechanical Engineering
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In Biomedical Engineering
In Control and Systems Engineering
- Yuke Zhu
- Li Fei-Fei
- Ajay Mandlekar
- Silvio Savarese
- Danfei Xu
- Roberto Mart'in-Mart'in
- Linxi (Jim) Fan
- Sergey Levine
In Mechanical Engineering
Citations in the corpus
- Human-in-the-Loop Imitation Learning using Remote Teleoperation (2020, background): “While similar systems have been built recently [4, 13], our system is the only one allowing for remote web-based operation and the only one demonstrated on contact-rich manipulation tasks.”
- iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks (2021, unknown): “Simulation environments with virtual reality interfaces: Researchers have used virtual reality 119 interfaces before to develop robotic solutions, by using VR to teleoperate real robots [28, 29, 30].”