In self-supervised robot learning, robots actively explore their environments and generate data by acting on entities in the environment. Therefore, an exploration policy is desired that ensures sample efficiency to minimize robot execution costs while still providing accurate learning. For this purpose, the robotic community has adopted Intrinsic Motivation (IM)-based approaches such as Learning Progress (LP). On the machine learning front, Active Learning (AL) has been used successfully, especially for classification tasks. In this work, we develop a novel AL framework geared towards robotics regression tasks, such as action-effect prediction and, more generally, for world model learning, which we call MUSEL - Model Uncertainty for Sample Efficient Learning. MUSEL aims to extract model uncertainty from the total uncertainty estimate given by a suitable learning engine by making use of earning progress and input diversity and use it to improve sample efficiency beyond the state-of-the-art action-effect prediction methods. We demonstrate the feasibility of our model by using a Stochastic Variational Gaussian Process (SVGP) as the learning engine and testing the system on a set of robotic experiments in simulation. The efficacy of MUSEL is demonstrated by comparing its performance to standard methods used in robot action-effect learning. In a robotic tabletop environment in which a robot manipulator is tasked with learning the effect of its actions, the experiments show that MUSEL facilitates higher accuracy in learning action effects while ensuring sample efficiency.

Reinforcement learning has been demonstrated as a flexible and effective approach for learning a range of continuous control tasks, such as those used by robots to manipulate objects in their environment. But in robotics particularly, real-world rollouts are costly, and sample efficiency can be a major limiting factor when learning a new skill. In game environments, the use of world models has been shown to improve sample efficiency while still achieving good performance, especially when images or other rich observations are provided. In this project, we explore the use of a world model in a deformable robotic manipulation task, evaluating its effect on sample efficiency when learning to fold a cloth in simulation. We compare the use of RGB image observation with a feature space leveraging built-in structure (keypoints representing the cloth configuration), a common approach in robot skill learning, and compare the impact on task performance and learning efficiency with and without the world model. Our experiments showed that the usage of keypoints increased the performance of the best model on the task by 50%, and in general, the use of a learned or constructed reduced feature space improved task performance and sample efficiency. The use of a state transition predictor(MDN-RNN) in our world models did not have a notable effect on task performance.