  This paper explores the problem of tool substitution, namely, identifying
substitute tools for performing a task from a given set of candidate tools. We
introduce a novel approach to tool substitution, that unlike prior work in the
area, combines both shape and material reasoning to effectively identify
substitute tools. Our approach combines the use of visual and spectral
reasoning using dual neural networks. It takes as input, the desired action to
be performed, and outputs a ranking of the available candidate tools based on
their suitability for performing the action. Our results on a test set of 30
real-world objects show that our approach is able to effectively match shape
and material similarities, with improved tool substitution performance when
combining both.
