Chelsea discusses the use of model-based reinforcement learning methods to predict outcomes based on robot actions. By leveraging a learned model, robots can plan and adapt their behavior in real-time to accomplish tasks such as manipulating objects. Future work aims to incorporate reward annotations to enhance the training of optimization algorithms and explore model-free methods.
