Robotics is associated with a high level of complexity in terms of behavior, which is difficult to hand engineer nor exhaustive enough to approach a task using supervised learning. Thus, reinforcement learning provides the kind of framework to capture such complex behavior.
Any task related to robotics is represented by high dimensional, continuous state, and action spaces. The environmental state is not fully observable. Learning in simulation alone is not enough to say the reinforcement learning agent is ready for the real world. In the case of robotics, a reinforcement learning agent should experience uncertainty in the real-world scenario but it's difficult and expensive to obtain and reproduce.
Robustness is the highest priority for robotics. In normal analytics or traditional machine learning problems, minor errors in data, pre-processing, or algorithms result in a significant change in behavior, especially for dynamic tasks. Thus, robust algorithms...