Interactive Hierarchical Task Learning via Crowdsourcing for Robot Adaptability

This paper describes the application of crowdsourcing to the problem of interactive task learning with the aim of enabling remotely-located users to effectively teach robots to perform complex tasks in real-world environments. We present a novel system that allows users to demonstrate hierarchical tasks via web-based control of a table-mounted six degree of freedom robot arm. The system employs intelligent action grouping suggestions and substitution suggestions for error recovery to assist the user in providing quality demonstrations. In addition, we describe design considerations and proposed extensions for the effective application of crowdsourcing to several human-robot interaction scenarios as motivated by this initial study.

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