Generative modelling for robot locomotion and manipulation

点击翻译按钮获取中文摘要

Brief description of the context of the research including potential impact:Deep generative models are increasingly a popular choice for representation learning. The aim of my PhD is to apply these methods to robotics. These models have shown the potential to allow robots to reason about their environments at an object-centric level. This means that robots will be able to interact naturally with their environment. I am working on legged robot locomotion and manipulation of robotic arms. The impact of my research will be to allow legged robots to perform search and rescue or inspection tasks in environments too dangerous for humans. Work applied to robot arms will have an impact in areas from health care to manufacturing and warehousing. Aims and ObjectivesThe aim of this research is to improve how robots interact and reason about the environments they work in. The objectives are to have robots perform tasks that humans take for granted, such as being able to pack delicate objects into a shipping box or walking over rough terrain.Novelty of the research methodologyCurrent techniques which allow robots to interact with their environments heavily rely on assumptions which are only valid for very specific scenarios. Generative models rationalise over all the data that they are trained on. This is a very attractive property of these models when applied to robotics.Alignment to EPSRC's strategies and research areasThis project falls directly into both the EPSRC robotics and artificial intelligence research areas. To have robots solve intricate problems and reason about their environments requires research into machine learning to create some sort of artificial intelligence.