Learning Robotic Navigation and Interaction from Object-based Semantic Maps

我们的项目旨在开发新的学习算法，使机器人能够执行目前不可能完成的高复杂性任务。与依赖低级传感器数据的现有方法相比，我们的目标是通过学习捕获语义、可视性和几何形状的环境的高级图形表示来实现这一目标。结果将是机器人能够使用人类指令有效地学习复杂的交互和导航行为，这些行为可以转移到看不见的环境中。我们的研究应该有利于在经济和社会重要领域的新应用，这些领域目前对机器人助手来说过于复杂、不安全、不确定，比如老年护理、先进制造和家用机器人。

Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently learn complex interaction and navigation behaviours that transfer to unseen environments. Our research should benefit new applications in domains of economic and societal importance that are currently too complex, unsafe, and uncertain for robot assistants, such as aged care, advanced manufacturing and domestic robotics.