Robot Programming by Demonstration with Crowdsourced Action Fixes
Robot Programming by Demonstration with Crowdsourced Action Fixes
复制标题
通过众包操作修复进行演示机器人编程
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Rajesh P. N. Rao
中科院分区：
文献类型：
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作者：
Maxwell Forbes;M. Chung;M. Cakmak;Rajesh P. N. Rao
通过演示编程(PBD)可以允许最终用户只需演示机器人就可以教会它们新的动作。然而，学习可概括的操作需要大量的演示，这对于最终用户来说是不合理的。在本文中，我们探索了使用众包来从人群中收集动作演示的想法。我们提出了一种PBD框架，其中最终用户提供初始种子演示，然后机器人搜索动作不起作用的场景，并请求人群针对这些场景修复动作。我们使用基于实例的学习和简单但功能强大的动作表示，允许直观地显示动作。群组工作人员直接与这些可视化效果交互以修复它们。我们通过一个涉及当地群众工作者(N=31)的用户研究演示了我们方法的实用性，并分析了收集的数据和替代设计参数的影响，以便为我们的系统的真实部署提供信息。
Programming by Demonstration (PbD) can allow end-users to teach robots new actions simply by demonstrating them. However, learning generalizable actions requires a large number of demonstrations that is unreasonable to expect from end-users. In this paper, we explore the idea of using crowdsourcing to collect action demonstrations from the crowd. We propose a PbD framework in which the end-user provides an initial seed demonstration, and then the robot searches for scenarios in which the action will not work and requests the crowd to fix the action for these scenarios. We use instance-based learning with a simple yet powerful action representation that allows an intuitive visualization of the action. Crowd workers directly interact with these visualizations to fix them. We demonstrate the utility of our approach with a user study involving local crowd workers (N=31) and analyze the collected data and the impact of alternative design parameters so as to inform a real-world deployment of our system.