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Adaptive Planning with Evidence Based Prediction for Improved Fluency in Routine Human-Robot Collaborative Tasks

机译:基于证据预测的自适应规划,提高流利程度的常规人机协作任务

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摘要

Robotics has seen widespread use in manufacturing environments, particularly in the automotive sector, where their introduction has alleviated costs, reduced workload and improved throughput. However, limitations exist in what can be automated, and tasks remain in which the human is not only required, but of crucial importance to the success of the task. In such scenarios, a dynamic interface exists between humans and machines, in which a form of collaboration is required to ensure effective and fluent interaction. From the perspective of the human, the effectiveness of this collaboration can be assessed on several criteria, such as trust and fluency (Hoffman 2013).
机译:机器人在制造环境中广泛使用,特别是在汽车领域,他们的引入减轻了成本,减少工作量和提高的吞吐量。 但是,可以在可以自动化的内容中存在限制,并且仍然是人类不仅需要的任务,而且对任务成功至关重要。 在这种情况下,人和机器之间存在动态接口,其中需要一种合作形式以确保有效和流畅的相互作用。 从人类的角度来看,可以对若干标准进行评估,例如信任和流利(Hoffman 2013),可以评估该合作的有效性。

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