Future versatile robots need the ability to learn new tasks and behaviors from demonstrations. Recent advances in virtual and augmented reality position these technologies as great candidates for the efficient and intuitive collection of large sets of demonstrations. While there are different possible approaches to control a virtual robot there has not yet been an evaluation of these control interfaces in regards to their efficiency and intuitiveness. These characteristics become particularly important when working with non-expert users and complex manipulation tasks. To this end, this work investigates five different interfaces to control a virtual robot in a comprehensive user study across various virtualized tasks in an AR setting. These interfaces include Hand Tracking, Virtual Kinesthetic Teaching, Gamepad and Motion Controller. Additionally, this work introduces Kinesthetic Teaching as a novel interface to control virtual robots in AR settings, where the virtual robot mimics the movement of a real robot manipulated by the user. This study reveals valuable insights into their usability and effectiveness. It shows that the proposed Kinesthetic Teaching interface significantly outperforms other interfaces in both objective and subjective metrics based on success rate, task completeness, and completion time and User Experience Questionnaires (UEQ+).

One of the biggest challenges to modern deep reinforcement learning (DRL) algorithms is sample efficiency. Many approaches learn a world model in order to train an agent entirely in imagination, eliminating the need for direct environment interaction during training. However, these methods often suffer from either a lack of imagination accuracy, exploration capabilities, or runtime efficiency. We propose HIEROS, a hierarchical policy that learns time abstracted world representations and imagines trajectories at multiple time scales in latent space. HIEROS uses an S5 layer-based world model, which predicts next world states in parallel during training and iteratively during environment interaction. Due to the special properties of S5 layers, our method can train in parallel and predict next world states iteratively during imagination. This allows for more efficient training than RNN-based world models and more efficient imagination than Transformer-based world models. We show that our approach outperforms the state of the art in terms of mean and median normalized human score on the Atari 100k benchmark, and that our proposed world model is able to predict complex dynamics very accurately. We also show that HIEROS displays superior exploration capabilities compared to existing approaches.