An Introspection Approach to Self-Awareness in Robotics

这个博士项目是关于研究机器人自我意识的计算内省。主要目标是设计一种方法来内省可解释的人工自我意识。计算内省将使机器人能够理解它们的局限性，相应地进行调整，并对自己甚至如何获得新知识进行推理。结合感官信息，具有自我意识的机器人将能够看到和理解自己的身体，并调整它以更好地适应给定的任务。因此，这个博士学位建立在这样一个概念的基础上，即通过了解自己，机器人可以重新配置自己的能力，以更好地适应他们在他们行动的环境中服务的目的，在考虑他们目前的性能和他们的机械限制。因此，具有自我意识的机器人在任务执行过程中需要较少的直接专家人工干预。

This PhD project is about investigating computational introspection for self-awareness in robotics. The main goal is to devise an approach to introspection for interpretable artificial self-awareness. Computational introspection will allow robots to understand their limitations, adapt accordingly and reason about themselves or even how to gain new knowledge. In combination with sensory information, self-aware robots will be able see and understand their body and adjust it to better suite a given task. Therefore, this PhD builds on the notion that through understanding themselves, robots can reconfigure their abilities to better suit the purpose they serve in the environment in which they act, after considering their current performance and their mechanical limitations. Self-aware robots will therefore require less direct expert human intervention during task execution.