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

Abstract

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.

Characterization of Indicators for Adaptive Human-Swarm Teaming

“…This group of metrics has a strong connection to the level of autonomy that the swarm component possesses. In a shared control situation where there is a possibility for negotiation between human and automation, it is essential to identify extra measures such as the percentage of requests for assistance created by controlling agents ( DelPreto et al, 2020 ; Kerzel et al, 2020 ), the percentage of requests for assistance created by the human operator, and the number of insignificant interventions by the human operator ( Steinfeld et al, 2006 ).…”

A Bayesian tracker for synthesizing mobile robot behaviour from demonstration

“…Another way of splitting is by probabilistic models. Daniel et al (2012) proposes to use a mixture of motion primitive combined through a hidden option variable. While the number of motion primitive and their shapes are specified by hand, their parameters and the relation between the states (option values) are learnt.…”

User-centred design in industrial collaborative automated systems

“…The human operator is a predominant agent in all programming techniques by either manipulating the robot, demonstrating and teaching trajectories and operations, or as the end-user of complex programming software and UIs. Virtual reality interfaces offer a repeatable, scalable, controlled and safe test ground for innovative programming methods such as kinesthetic teaching [16] and user monitoring, and it can be applicable to older generations of robots.…”