Interactive Learning of Temporal Features for Control: Shaping Policies and State Representations From Human Feedback
Interactive Learning of Temporal Features for Control: Shaping Policies and State Representations From Human Feedback
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DOI：
10.1109/mra.2020.2983649
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发表时间：
2020-04
影响因子：
5.7
通讯作者：
Rodrigo Pérez-Dattari;C. Celemin;Giovanni Franzese;Javier Ruiz-del-Solar;Jens Kober
中科院分区：
文献类型：
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作者：
Rodrigo Pérez-Dattari;C. Celemin;Giovanni Franzese;Javier Ruiz-del-Solar;Jens Kober
目前正在进行的工业革命要求更灵活的产品，包括家庭环境和中型工厂中的机器人。这样的机器人应该能够适应新的条件和环境，并能够轻松地进行编程。例如，让我们假设有机器人机械手在工业生产线上工作，它们需要执行一项新任务。如果这些机器人是硬编码的，可能需要几天时间才能使它们适应新的设置，这将使工厂停止生产。非专业人士可以轻松编程的机器人将大大加快这一过程
Current ongoing industry revolution demands more flexible products, including robots in household environments and medium-scale factories. Such robots should be able to adapt to new conditions and environments and be programmed with ease. As an example, let us suppose that there are robot manipulators working on an industrial production line and that they need to perform a new task. If these robots were hard coded, it could take days to adapt them to the new settings, which would stop production at the factory. Robots that non-expert humans could easily program would speed up the process considerably