Object recall using an experience database to accelerate robot action planning

摘要

Robot interaction planning is a computationally expensive process which rarely makes use of previous experiences in a deliberative manner. This paper addresses this issue by examining dimensionality reduction techniques to allow comparison of objects in a robot's environment based on the way they react to robot manipulation. We compare a number of techniques which can map objects from an observation space, which may contain thousands of dimensions, to a lower dimensionality space - the embedding space - which allows objects to be compared in an efficient manner, making knowledge transfer between similar objects more computationally tractable.