  State-of-the-art object grasping methods rely on depth sensing to plan robust
grasps, but commercially available depth sensors fail to detect transparent and
specular objects. To improve grasping performance on such objects, we introduce
a method for learning a multi-modal perception model by bootstrapping from an
existing uni-modal model. This transfer learning approach requires only a
pre-existing uni-modal grasping model and paired multi-modal image data for
training, foregoing the need for ground-truth grasp success labels nor real
grasp attempts. Our experiments demonstrate that our approach is able to
reliably grasp transparent and reflective objects. Video and supplementary
material are available at
https://sites.google.com/view/transparent-specular-grasping.
