The Effects of Finger-Walking in Place (FWIP) on Spatial Knowledge Acquisition in Virtual Environments

Spatial knowledge, necessary for efficient navigation, comprises route knowledge (memory of landmarks along a route) and survey knowledge (overall representation like a map). Virtual environments (VEs) have been suggested as a power tool for understanding some issues associated with human navigation, such as spatial knowledge acquisition. The Finger-Walking-in-Place (FWIP) interaction technique is a locomotion technique for navigation tasks in immersive virtual environments (IVEs). The FWIP was designed to map a human’s embodied ability overlearned by natural walking for navigation, to finger-based interaction technique. Its implementation on Lemur and iPhone/iPod Touch devices was evaluated in our previous studies. In this paper, we present a comparative study of the joystick’s flying technique versus the FWIP. Our experiment results show that the FWIP results in better performance than the joystick’s flying for route knowledge acquisition in our maze navigation tasks

Two-handed navigation in a haptic virtual environment

This paper describes the initial results from a study looking at a two-handed interaction paradigm for tactile navigation for blind and visually impaired users. Participants were set the task of navigating a virtual maze environment using their dominant hand to move the cursor, while receiving contextual information in the form of tactile cues presented to their non-dominant hand. Results suggest that most participants were comfortable with the two-handed style of interaction even with little training. Two sets of contextual cues were examined with information presented through static patterns or tactile flow of raised pins. The initial results of this study suggest that while both sets of cues were usable, participants performed significantly better and faster with the static cues

Reinforcement Learning using Augmented Neural Networks

Neural networks allow Q-learning reinforcement learning agents such as deep Q-networks (DQN) to approximate complex mappings from state spaces to value functions. However, this also brings drawbacks when compared to other function approximators such as tile coding or their generalisations, radial basis functions (RBF) because they introduce instability due to the side effect of globalised updates present in neural networks. This instability does not even vanish in neural networks that do not have any hidden layers. In this paper, we show that simple modifications to the structure of the neural network can improve stability of DQN learning when a multi-layer perceptron is used for function approximation.Comment: 7 pages; two columns; 4 figure

Dissociable representations of environmental size and complexity in the human hippocampus

The hippocampus is widely assumed to play a central role in representing spatial layouts in the form of "cognitive maps." It remains unclear, however, which properties of the world are explicitly encoded in the hippocampus, and how these properties might contribute to the formation of cognitive maps. Here we investigated how physical size and complexity, two key properties of any environment, affect memory-related neural activity in the human hippocampus. We used functional magnetic resonance imaging and a virtual maze-learning task to examine retrieval-related activity for three previously learned virtual mazes that differed systematically in their physical size and complexity (here defined as the number of distinct paths within the maze). Before scanning, participants learned to navigate each of the three mazes; hippocampal activity was then measured during brief presentations of static images from within each maze. Activity within the posterior hippocampus scaled with maze size but not complexity, whereas activity in the anterior hippocampus scaled with maze complexity but not size. This double dissociation demonstrates that environmental size and complexity are explicitly represented in the human hippocampus, and reveals a functional specialization for these properties along its anterior-posterior axis

An emergent wall following behaviour to escape local minima for swarms of agents

Natural examples of emergent behaviour, in groups due to interactions among the group's individuals, are numerous. Our aim, in this paper, is to use complex emergent behaviour among agents that interact via pair-wise attractive and repulsive potentials, to solve the local minima problem in the artificial potential based navigation method. We present a modified potential field based path planning algorithm, which uses agent internal states and swarm emergent behaviour to enhance group performance. The algorithm is used successfully to solve a reactive path-planning problem that cannot be solved using conventional static potential fields due to local minima formation. Simulation results demonstrate the ability of a swarm of agents to perform problem solving using the dynamic internal states of the agents along with emergent behaviour of the entire group