Title: Dynamics-aware Skill Generation from Behaviourally Diverse Demonstrations. Abstract: Learning from demonstrations (LfD) provides a data-efficient way for a robot to learn a task by observing humans performing the task, without the need for an explicit reward function. However, in many real-world scenarios (e.g., driving a car) humans often perform the same task in different ways, motivated not only by the primary objective of the task (e.g., reaching the destination safely) but also by their individual preferences (e.g., different driving behaviours), leading to a multi-modal distribution of demonstrations. In this work, we consider an LfD problem, where the reward function for the main objective of the task is known to the learning agent; however, the individual preferences leading to the variations in the demonstrations are unknown. We show that current LfD approaches learn policies that either track a single mode or the mean of the demonstration distribution. In contrast, we propose an algorithm to learn a diverse set of policies to perform the task, capturing the different modes in the demonstrations due to the diverse preferences of the individuals. We show that we can build a parameterised solution space that captures different behaviour patterns from the demonstrations. Then, a set of policies can be generated in solution space that generate a diverse range of behaviours that go beyond the provided demonstrations.