Representations of the city in video games

This research strives to characterize the means by which video game players experience and understand the space of the game city during the course of play. Three-dimensional video game cities are neither static environments nor stationary views; rather, they are experienced through movement, action, and play. Our experiences of new places are not developed at a glance. Instead, they are cultivated through use over time. This work utilizes games that take place in constructed versions of New York City as a case study. By focusing on the ways players navigate spaces, we can understand how they construct spatial awareness and how this space is transformed into a meaningful place of play. In order to come to this understanding, this study asks a series of questions: How are these spaces arranged? How does the player move through the space and how does the game teach spatial navigation? What actions are performed in the space and how is gameplay adapted for the city environment? And how do of narrative environments contribute to a player's identification with the space? These questions are examined within a framework of urban, cultural, and game studies. I examine techniques that are employed by video game city designers to help players navigate space and make it meaningful. Additionally, this research poses areas for future expansion and experimentation with game cities.M.S.Committee Chair: Pearce, Celia; Committee Member: Do, Ellen Yi-Luen; Committee Member: Knoespel, Kenneth; Committee Member: Nitsche, Michae

Addressing Tasks Through Robot Adaptation

Developing flexible, broadly capable systems is essential for robots to move out of factories and into our daily lives, functioning as responsive agents that can handle whatever the world throws at them. This dissertation focuses on two kinds of robot adaptation. Modular self-reconfigurable robots (MSRR) adapt to the requirements of their task and environments by transforming themselves. By rearranging the connective structure of their component robot modules, these systems can assume different morphologies: for example, a cluster of modules might configure themselves into a car to maneuver on flat ground, a snake to climb stairs, or an arm to pick and place objects. Conversely, environment augmentation is a strategy in which the robot transforms its environment to meet its own needs, adding physical structures that allow it to overcome obstacles. In both areas, the presented work includes elements of hardware design, algorithms, and integrated systems, with the common goal of establishing these methods of adaptation as viable strategies to address tasks. The research takes a systems-level view of robotics, placing particular emphasis on experimental validation in hardware

Supplementing Frequency Domain Interpolation Methods for Character Animation

The animation of human characters entails difficulties exceeding those met simulating objects, machines or plants. A person's gait is a product of nature affected by mood and physical condition. Small deviations from natural movement are perceived with ease by an unforgiving audience. Motion capture technology is frequently employed to record human movement. Subsequent playback on a skeleton underlying the character being animated conveys many of the subtleties of the original motion. Played-back recordings are of limited value, however, when integration in a virtual environment requires movements beyond those in the motion library, creating a need for the synthesis of new motion from pre-recorded sequences. An existing approach involves interpolation between motions in the frequency domain, with a blending space defined by a triangle network whose vertices represent input motions. It is this branch of character animation which is supplemented by the methods presented in this thesis, with work undertaken in three distinct areas. The first is a streamlined approach to previous work. It provides benefits including an efficiency gain in certain contexts, and a very different perspective on triangle network construction in which they become adjustable and intuitive user-interface devices with an increased flexibility allowing a greater range of motions to be blended than was possible with previous networks. Interpolation-based synthesis can never exhibit the same motion variety as can animation methods based on the playback of rearranged frame sequences. Limitations such as this were addressed by the second phase of work, with the creation of hybrid networks. These novel structures use properties of frequency domain triangle blending networks to seamlessly integrate playback-based animation within them. The third area focussed on was distortion found in both frequency- and time-domain blending. A new technique, single-source harmonic switching, was devised which greatly reduces it, and adds to the benefits of blending in the frequency domain

Planning Plausible Human Motions for Navigation and Collision Avoidance

This thesis investigates the plausibility of computer-generated human motions for navigation and collision avoidance. To navigate a human character through obstacles in an virtual environment, the problem is often tackled by finding the shortest possible path to the destination with smoothest motions available. This is because such solution is regarded as cost-effective and free-flowing in that it implicitly minimises the biomechanical efforts and potentially precludes anomalies such as frequent and sudden change of behaviours, and hence more plausible to human eyes. Previous research addresses this problem in two stages: finding the shortest collision-free path (motion planning) and then fitting motions onto this path accordingly (motion synthesis). This conventional approach is not optimal because the decoupling of these two stages introduces two problems. First, it forces the motion-planning stage to deliberately simplify the collision model to avoid obstacles. Secondly, it over-constrains the motion-synthesis stage to approximate motions to a sub-optimal trajectory. This often results in implausible animations that travel along erratic long paths while making frequent and sudden behaviour changes. In this research, I argue that to provide more plausible navigation and collision avoidance animation, close-proximity interaction with obstacles is crucial. To address this, I propose to combine motion planning and motion synthesis to search for shorter and smoother solutions. The intuition is that by incorporating precise collision detection and avoidance with motion capture database queries, we will be able to plan fine-scale interactions between obstacles and moving crowds. The results demonstrate that my approach can discover shorter paths with steadier behaviour transitions in scene navigation and crowd avoidance. In addition, this thesis attempts to propose a set of metrics that can be used to evaluate the plausibility of computer-generated navigation animations

Automatic Dense 3D Scene Mapping from Non-overlapping Passive Visual Sensors for Future Autonomous Systems

The ever increasing demand for higher levels of autonomy for robots and vehicles means there is an ever greater need for such systems to be aware of their surroundings. Whilst solutions already exist for creating 3D scene maps, many are based on active scanning devices such as laser scanners and depth cameras that are either expensive, unwieldy, or do not function well under certain environmental conditions. As a result passive cameras are a favoured sensor due their low cost, small size, and ability to work in a range of lighting conditions. In this work we address some of the remaining research challenges within the problem of 3D mapping around a moving platform. We utilise prior work in dense stereo imaging, Stereo Visual Odometry (SVO) and extend Structure from Motion (SfM) to create a pipeline optimised for on vehicle sensing. Using forward facing stereo cameras, we use state of the art SVO and dense stereo techniques to map the scene in front of the vehicle. With significant amounts of prior research in dense stereo, we addressed the issue of selecting an appropriate method by creating a novel evaluation technique. Visual 3D mapping of dynamic scenes from a moving platform result in duplicated scene objects. We extend the prior work on mapping by introducing a generalized dynamic object removal process. Unlike other approaches that rely on computationally expensive segmentation or detection, our method utilises existing data from the mapping stage and the findings from our dense stereo evaluation. We introduce a new SfM approach that exploits our platform motion to create a novel dense mapping process that exceeds the 3D data generation rate of state of the art alternatives. Finally, we combine dense stereo, SVO, and our SfM approach to automatically align point clouds from non-overlapping views to create a rotational and scale consistent global 3D model

Accelerating decision making under partial observability using learned action priors

Thesis (M.Sc.)--University of the Witwatersrand, Faculty of Science, School of Computer Science and Applied Mathematics, 2017.Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical framework allowing a robot to reason about the consequences of actions and observations with respect to the agent's limited perception of its environment. They allow an agent to plan and act optimally in uncertain environments. Although they have been successfully applied to various robotic tasks, they are infamous for their high computational cost. This thesis demonstrates the use of knowledge transfer, learned from previous experiences, to accelerate the learning of POMDP tasks. We propose that in order for an agent to learn to solve these tasks quicker, it must be able to generalise from past behaviours and transfer knowledge, learned from solving multiple tasks, between di erent circumstances. We present a method for accelerating this learning process by learning the statistics of action choices over the lifetime of an agent, known as action priors. Action priors specify the usefulness of actions in situations and allow us to bias exploration, which in turn improves the performance of the learning process. Using navigation domains, we study the degree to which transferring knowledge between tasks in this way results in a considerable speed up in solution times. This thesis therefore makes the following contributions. We provide an algorithm for learning action priors from a set of approximately optimal value functions and two approaches with which a prior knowledge over actions can be used in a POMDP context. As such, we show that considerable gains in speed can be achieved in learning subsequent tasks using prior knowledge rather than learning from scratch. Learning with action priors can particularly be useful in reducing the cost of exploration in the early stages of the learning process as the priors can act as mechanism that allows the agent to select more useful actions given particular circumstances. Thus, we demonstrate how the initial losses associated with unguided exploration can be alleviated through the use of action priors which allow for safer exploration. Additionally, we illustrate that action priors can also improve the computation speeds of learning feasible policies in a shorter period of time.MT201