  An autonomous robot should be able to evaluate the affordances that are
offered by a given situation. Here we address this problem by designing a
system that can densely predict affordances given only a single 2D RGB image.
This is achieved with a convolutional neural network (ResNet), which we combine
with refinement modules recently proposed for addressing semantic image
segmentation. We define a novel cost function, which is able to handle
(potentially multiple) affordances of objects and their parts in a pixel-wise
manner even in the case of incomplete data. We perform qualitative as well as
quantitative evaluations with simulated and real data assessing 15 different
affordances. In general, we find that affordances, which are well-enough
represented in the training data, are correctly recognized with a substantial
fraction of correctly assigned pixels. Furthermore, we show that our model
outperforms several baselines. Hence, this method can give clear action
guidelines for a robot.
