Intuitive Robot Intelligence: Efficiently Learning and Improving of Explainable Skills and Behaviors for Intuitive Human-Robot Interaction.

近年来，人工智能方法在广泛的领域和应用中产生了令人印象深刻的结果。这些成功加上对辅助生活，老年人护理和本地生产的需求不断增加，使人们期待智能自主机器人即将在我们的日常生活中部署。这些未来的智能体将有望在一般日常情况和专业任务中与非专家用户进行密切互动。在这两种情况下，这样的智能代理将不得不适应新的或修改的任务，在动态环境中，而不依赖于大量的数据，不能提供的非专家在数量或质量所需的当前方法。此外，最先进的机器学习方法无法以透明和可理解的方式表示学习的模型，行为和特征，导致人机协作的保证不足和额外的复杂性。新一代的智能机器人将被要求能够与非专家用户交流意图，并以自然的方式从用户的动作中理解意图。这些机器人对非专家用户来说将显得更直观，并且能够通过与非专家用户更直观的交互来推断有价值的信息。新一代的智能、直觉机器人将需要i）有效学习可解释和可理解的技能和行为，ii）从弱标记、次优演示中改进技能和行为，iii）通过直觉交互有效转移和适应技能和行为。本项目将在各种人机协作任务中研究这三个方面。

Artificial intelligence approaches have produced impressive results across a wide spectrum of fields and applications in recent years. These successes in combination with an increasing demand for assisted living, elderly care and local production have have caused the expectation of an imminent deployment of intelligent autonomous robots in our everyday life. These future agents will be expected to work in close interaction with non-expert users in both general everyday situations and professional tasks. In either scenario such intelligent agents will have to adapt to new or modified tasks in dynamic environments without relying on the huge amounts of data that can not be provided by non-experts in either the quantity or quality required by current approaches. Furthermore, state-of-the-art machine learning methods are not able to represent the learned models, behaviors and features in a transparent and comprehensible way resulting in processes with insufficient guarantees and additional complexity for human-machine collaborations. A new generation of intelligent robots will be required that is capable of communicating intent to non-expert users as well as understanding intent from action of the user in a natural way. These robots will appear more intuitive to non-expert users as well as being able to deduct valuable information through more intuitive interaction with the non-expert user. This new generation of intelligent, intuitive robots will requirei) efficient learning of explainable and comprehensible skills and behaviors,ii) skill and behavior improvement from weakly labeled, suboptimal demonstrations,iii) efficient transfer and adaptation of skills and behaviors through intuitive interaction.This project will investigate these three aspect in various human-robot collaboration tasks.