Abstract
The problem of performing everyday manipulation tasks robustly in open environments is currently beyond the capabilities of artificially intelligent robots; humans are required. The difficulty arises from the high variability in open environments; it is not feasible to program for, or train for, every variation. This correspondence paper presents the case for a new approach to the problem, based on three mutually dependent ideas: 1) highly transferable manipulation skills; 2) choice of representation: a scene can be modeled in several different ways; and 3) top-down processes by which the robot's task can influence the bottom-up processes interpreting a scene. The approach we advocate is supported by evidence from what we know about humans, and also the approach is implicitly taken by human designers in designing representations for robots. We present brief results of an implementation of these ideas in robot vision, and give some guidelines for how the key ideas can be implemented more generally in practical robot systems.