Real-time natural language corrections for assistive robotic manipulators
Real-time natural language corrections for assistive robotic manipulators
复制标题
辅助机器人操纵器的实时自然语言校正
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
B. Argall
中科院分区：
文献类型：
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作者：
Alexander Broad;Jacob Arkin;Nathan D. Ratliff;T. Howard;B. Argall
我们提出了一种可概括的自然语言界面，使用户可以实时向辅助机器人操纵器提供纠正措施，这是由于渴望改善人类与机器人之间的合作的动机。他们的机器人对手如何实现目标，从而通过增加人类伴侣的优势（例如，视力和环境知识）来增加系统的效用。这项工作适用于我们的自然语言接口和没有残疾的用户。通过将修饰视为运动生成（计划）范式的限制，改变了机器人操纵器的行为。用户可能希望纠正其辅助操纵器的行为，我们使用亚马逊机械Turk收集的数据来捕获人们用来描述所需更正的术语的全面样本。 - 使用开源语音到文本软件和kinova机器人机器人臂的系统。不熟悉机器人系统的用户并分析故障点和未来方向。
We propose a generalizable natural language interface that allows users to provide corrective instructions to an assistive robotic manipulator in real-time. This work is motivated by the desire to improve collaboration between humans and robots in a home environment. Allowing human operators to modify properties of how their robotic counterpart achieves a goal on-the-fly increases the utility of the system by incorporating the strengths of the human partner (e.g. visual acuity and environmental knowledge). This work is applicable to users with and without disability. Our natural language interface is based on the distributed correspondence graph, a probabilistic graphical model that assigns semantic meaning to user utterances in the context of the robot’s environment and current behavior. We then use the desired corrections to alter the behavior of the robotic manipulator by treating the modifications as constraints on the motion generation (planning) paradigm. In this paper, we highlight four dimensions along which a user may wish to correct the behavior of his or her assistive manipulator. We develop our language model using data collected from Amazon Mechanical Turk to capture a comprehensive sample of terminology that people use to describe desired corrections. We then develop an end-to-end system using open-source speech-to-text software and a Kinova Robotics MICO robotic arm. To demonstrate the efficacy of our approach, we run a pilot study with users unfamiliar with robotic systems and analyze points of failure and future directions.