Generalizable Robotic Imitation Learning: Interactive Learning and Inductive Bias

Robots have the potential to assume tasks across various real-world scenarios. To achieve this, we require adaptable and reactive robots that can robustly deal with products and environments that present variability. For example, in the agro-food sector, each tomato plant inside a greenhouse is unique; hence, different robotic motions are required when interacting with different plants. Unfortunately, due to their simplicity, most robotic solutions currently employed are rigid and rely on hand-crafted rules. Such solutions perform well in controlled and repetitive environments; however, they fall short when these conditions are not met. As a consequence, a large family of problems remains unsolved.....