  Future service robots working in human environments, such as kitchens, will
face situations where they need to improvise. The usual tool for a given task
might not be available and the robot will have to use some substitute tool. The
robot needs to select an appropriate alternative tool from the candidates
available, and also needs to know where to grasp it, how to orient it and what
part to use as the end-effector. We present a system which takes as input a
candidate tool's point cloud and weight, and outputs a score for how effective
that tool is for a task, and how to use it. Our key novelty is in taking a
task-driven approach, where the task exerts a top-down influence on how low
level vision data is interpreted. This facilitates the type of 'everyday
creativity' where an object such as a wine bottle could be used as a rolling
pin, because the interpretation of the object is not fixed in advance, but
rather results from the interaction between the bottom-up and top-down
pressures at run-time. The top-down influence is implemented by transfer: prior
knowledge of geometric features that make a tool good for a task is used to
seek similar features in a candidate tool. The prior knowledge is learned by
simulating Web models performing the tasks. We evaluate on a set of fifty
household objects and five tasks. We compare our system with the closest one in
the literature and show that we achieve significantly better results
