Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.

In this experiment, a haptic glove with vibratory motors on the fingertips was tested against the standard HTC Vive controller to see if the additional vibrations provided by the glove increased immersion in common gaming scenarios where haptic feedback is provided. Specifically, two scenarios were developed: an explosion scene containing a small and large explosion and a box interaction scene that allowed the participants to touch the box virtually with their hand. At the start of this project, it was hypothesized that the haptic glove would have a significant positive impact in at least one of these scenarios. Nine participants took place in the study and immersion was measured through a post-experiment questionnaire. Statistical analysis on the results showed that the haptic glove did have a significant impact on immersion in the box interaction scene, but not in the explosion scene. In the end, I conclude that since this haptic glove does not significantly increase immersion across all scenarios when compared to the standard Vive controller, it should not be used at a replacement in its current state.

This thesis is based on bringing together three different components: non-Euclidean geometric worlds, virtual reality, and environmental puzzles in video games. While all three exist in their own right in the world of video games, as well as combined in pairs, there are virtually no examples of all three together. Non-Euclidean environmental puzzle games have existed for around 10 years in various forms, short environmental puzzle games in virtual reality have come into existence in around the past five years, and non-Euclidean virtual reality exists mainly as non-video game short demos from the past few years. This project seeks to be able to bring these components together to create a proof of concept for how a game like this should function, particularly the integration of non-Euclidean virtual reality in the context of a video game. To do this, a Unity package which uses a custom system for creating worlds in a non-Euclidean way rather than Unity’s built-in components such as for transforms, collisions, and rendering was used. This was used in conjunction with the SteamVR implementation with Unity to create a cohesive and immersive player experience.