  Over the past few years, deep learning techniques have achieved tremendous
success in many visual understanding tasks such as object detection, image
segmentation, and caption generation. Despite this thriving in computer vision
and natural language processing, deep learning has not yet shown significant
impact in robotics. Due to the gap between theory and application, there are
many challenges when applying the results of deep learning to the real robotic
systems. In this study, our long-term goal is to bridge the gap between
computer vision and robotics by developing visual methods that can be used in
real robots. In particular, this work tackles two fundamental visual problems
for autonomous robotic manipulation: affordance detection and fine-grained
action understanding. Theoretically, we propose different deep architectures to
further improves the state of the art in each problem. Empirically, we show
that the outcomes of our proposed methods can be applied in real robots and
allow them to perform useful manipulation tasks.
