Smart environments can already observe the actions of a human through pervasive sensors. Based on these observations, our work aims to predict the actions a human is likely to perform next. Predictions can enable a robot to proactively assist humans by autonomously executing an action on their behalf. In this paper, Action Graphs are introduced to model the order constraints between actions. Action Graphs are derived from a problem defined in Planning Domain Definition Language (PDDL). When an action is observed, the node values are updated and next actions predicted. Subsequently, a robot executes one of the predicted actions if it does not impact the flow of the human by obstructing or delaying them. Our Action Graph approach is applied to a kitchen domain.

中文翻译：

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在智能环境中主动提供机器人协助的动作图

智能环境已经可以通过普适的传感器观察人类的行为。基于这些观察，我们的工作旨在预测人类下一步可能执行的动作。预测可以使机器人通过代表人类自主执行动作来主动地协助人类。在本文中，引入了动作图以对动作之间的顺序约束进行建模。操作图源自“规划域定义语言（PDDL）”中定义的问题。观察到动作后，将更新节点值并预测下一个动作。随后，如果机器人没有通过阻止或延迟它们来影响人流，则机器人将执行其中之一。我们的动作图方法适用于厨房领域。