  Vision-based learning methods provide promise for robots to learn complex
manipulation tasks. However, how to generalize the learned manipulation skills
to real-world interactions remains an open question. In this work, we study
robotic manipulation skill learning from a single third-person view
demonstration by using activity recognition and object detection in computer
vision. To facilitate generalization across objects and environments, we
propose to use a prior knowledge base in the form of a text corpus to infer the
object to be interacted with in the context of a robot. We evaluate our
approach in a real-world robot, using several simple and complex manipulation
tasks commonly performed in daily life. The experimental results show that our
approach achieves good generalization performance even from small amounts of
training data.
