Having established why social robots are important and why we care about them, we now argue for why learning – and in particular, learning from humans – is an important problem in social robotics. The first reason is that without learning, robots cannot adapt to their environments. This problem becomes especially dire in the real world because the real world is changing all the time. The robot’s workplace, the people it interacts with, and even the tasks that we want it to do may change overtime. A robot that does not learn cannot adapt to these changes and will require reprogramming, which would make robot ownership and maintenance an extremely tedious task. Another reason for why we want robots to learn is because for certain types of robots, learning is part of their appeal. A pet robot, for example, will have a hard time holding a person’s attention for long if it simply acted in a scripted manner and never adapted its interactions based on the person’s responses. One solution to this problem is to make the robot act randomly – the robot would become unpredictable, but at the same time it’d become inconsistent, which lessens people’s incentive to treat the robot as a social entity. Plus, acting randomly does not allow for the person’s interactions with the robot to be taken into account, which decreases people’s desire to form a relationship with the robot. Learning robots avoid this pitfall by being dynamic in a consistent manner, and by taking into account people’s interactions. As to why we want social robots to learn from humans, the reason can be summarized as: humans have a lot to teach. As research in robot task learning by demonstration has shown, robot learning can be significantly facilitated by human input. Moreover, humans are a great source for the types of social rules that we want robots to learn, since the robots are operating in those very same environments.