Reinforcement learning with model-based feedforward inputs for robotic table tennis

We rethink the traditional reinforcement learning approach, which is based on optimizing over feedback policies, and propose a new framework that optimizes over feedforward inputs instead. This not only mitigates the risk of destabilizing the system during training but also reduces the bulk of the learning to a supervised learning task. As a result, efficient and well-understood supervised learning techniques can be applied and are tuned using a validation data set. The labels are generated with a variant of iterative learning control, which also includes prior knowledge about the underlying dynamics. Our framework is applied for intercepting and returning ping-pong balls that are played to a four-degrees-of-freedom robotic arm in real-world experiments. The robot arm is driven by pneumatic artificial muscles, which makes the control and learning tasks challenging. We highlight the potential of our framework by comparing it to a reinforcement learning approach that optimizes over feedback policies. We find that our framework achieves a higher success rate for the returns ( 100 % vs. 96 % , on 107 consecutive trials, see https://youtu.be/kR9jowEH7PY ) while requiring only about one tenth of the samples during training. We also find that our approach is able to deal with a variant of different incoming trajectories.