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

点击翻译按钮获取中文摘要

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.