In recent years there has been an increasing interest in deploying robotic systems in public environments able to effectively interact with people. To properly work in the wild, such systems should be robust and be able to deal with complex and unpredictable events that seldom happen in controlled laboratory conditions. Moreover, having to deal with untrained users adds further complexity to the problem and makes the task of defining effective interactions especially difficult. In this work, a Cognitive System that relies on planning is extended with adaptive capabilities and embedded in a Tiago robot. The result is a system able to help a person to complete a predefined game by offering various degrees of assistance. The robot may decide to change the level of assistance depending on factors such as the state of the game or the user performance at a given time. We conducted two days of experiments during a public fair. We selected random users to interact with the robot and only for one time. We show that, despite the short-term nature of human–robot interactions, the robot can effectively adapt its way of providing help, leading to better user performances as compared to a robot not providing this degree of flexibility.

中文翻译：

---

现实环境中的短期人机交互适应性

近年来，人们越来越关注在能够有效与人互动的公共环境中部署机器人系统。为了在野外正常工作，此类系统应具有强大的功能，并能够处理在受控实验室条件下很少发生的复杂且不可预测的事件。此外，必须与未经培训的用户打交道，使问题进一步复杂化，并使定义有效交互的任务特别困难。在这项工作中，依赖于计划的认知系统通过自适应功能得到扩展，并嵌入到Tiago机器人中。结果是一种能够通过提供各种程度的帮助来帮助人们完成预定义游戏的系统。机器人可以决定根据给定时间的游戏状态或用户表现等因素来更改辅助级别。我们在公开交易会上进行了为期两天的实验。我们选择了随机用户与机器人互动，并且只能互动一次。我们显示，尽管人机交互的短期性质，但与不提供这种灵活性的机器人相比，机器人可以有效地调整其提供帮助的方式，从而带来更好的用户性能。