We as humans are very good at generalizing over our own previous experiences to deal with new, unexpected situations, and learn continuously over time about new things. To do so, we need to make new experiences, sometimes make mistakes, and learn from them. All of this is long, expensive and possibly dangerous for robots. The project RoboExNovo (Robots learning about objects from externalized knowledge sources) approaches this problem bringing a key insight: that robots can learn about what they don't know by searching on the Web, exactly as we do. This would have deep effects on the degree of autonomy that a robot can have: if it is sees a cereal box that it has never seen before in the grocery delivery box, it can look it up on the web, learn autonomously what it is, and store it in the right cupboard without human supervision; and so forth.
To make this possible, RoboExNovo has achieved several breakthroughs, from developing the first algorithm for bridging between web stored perceptual knowledge, like RGB images, and robot knowledge, like depth maps to introducing the first approach for creating new robot memories about unknown objects by automatic web mining; from introducing the theory and algorithms for online learning of new things and experiences in deep robot perception, to the first deep architecture able to teach robots how to learn from Web data by solving self-created riddles and jigsaw puzzles.