Interactive Hierarchical Task Learning via Crowdsourcing for Robot Adaptability
Interactive Hierarchical Task Learning via Crowdsourcing for Robot Adaptability
复制标题
通过众包进行交互式分层任务学习以提高机器人适应性
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
S. Chernova
中科院分区：
文献类型：
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作者：
A. Clair;Carl Saldanha;Adrian Boteanu;S. Chernova
本文介绍了应用众包的交互式任务学习的问题，其目的是使远程用户有效地教机器人在现实世界的环境中执行复杂的任务。我们提出了一种新的系统，允许用户通过基于Web的控制台安装的六个自由度的机器人手臂演示分层任务。该系统采用智能动作分组的建议和替换错误恢复的建议，以帮助用户提供高质量的演示。此外，我们描述了设计考虑和提出的扩展有效的应用众包的几个人-机器人交互的情况下，由这个初步的研究。
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.