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Studies involving the group predator behavior of wolves have inspired multiple robotic architectures to mimic these biological behaviors in their designs and research. In this work, we aim to use robotic systems to mimic wolf packs' single and group behavior. This work aims to extend the original research by Weitzenfeld et al [7] and evaluate under a new multi-robot robot system architecture. The multiple robot architecture includes a 'Prey' pursued by a wolf pack consisting of an 'Alpha' and 'Beta' robotic group. The Alpha Wolf' will be the group leader, searching and tracking the 'Prey.' At the same time, the multiple Beta 'Wolves' will follow behind the Alpha, tracking and maintaining a set distance in the formation. The robotic systems used are multiple raspberry pi-robots designed in the USF bio-robotics lab that use a combination of color cameras and distance sensors to assist the Beta 'Wolves' in keeping a set distance between the Alpha "Wolf" and themselves. Several experiments were performed in simulation, using Webots, and with physical robots. An analysis was done comparing the performance of the physical robot in the real world to the virtual robot in the simulated environment. 

In the last decade, studies have demonstrated that hippocampal place cells influence rats’ navigational learning ability. Moreover, researchers have observed that place cell sequences associated with routes leading to a reward are reactivated during rest periods. This phenomenon is known as Hippocampal Replay, which is thought to aid navigational learning and memory consolidation. These findings in neuroscience have inspired new robot navigation models that emulate the learning process of mammals. This study presents a novel model that encodes path information using place cell connections formed during online navigation. Our model employs these connections to generate sequences of state-action pairs to train our actor-critic reinforcement learning model offline. Our results indicate that our method can accelerate the learning process of solving an open-world navigational task. Specifically, we demonstrate that our approach can learn optimal paths through open-field mazes with obstacles. 

Extensive studies in rodents show that place cells in the hippocampus have firing patterns that are highly correlated with the animal's location in the environment and are organized in layers of increasing field sizes or scales along its dorsoventral axis. In this study, we use a spatial cognition model to show that different field sizes could be exploited to adapt the place cell representation to different environments according to their size and complexity. Specifically, we provide an in-depth analysis of how to distribute place cell fields according to the obstacles in cluttered environments to optimize learning time and path optimality during goal-oriented spatial navigation tasks. The analysis uses a reinforcement learning (RL) model that assumes that place cells allow encoding the state. While previous studies have suggested exploiting different field sizes to represent areas requiring different spatial resolutions, our work analyzes specific distributions that adapt the representation to the environment, activating larger fields in open areas and smaller fields near goals and subgoals (e.g., obstacle corners). In addition to assessing how the multi-scale representation may be exploited in spatial navigation tasks, our analysis and results suggest place cell representations that can impact the robotics field by reducing the total number of cells for path planning without compromising the quality of the paths learned. 

We show how hippocampal replay could explain latent learning, a phenomenon observed in animals where unrewarded pre-exposure to an environment, i.e. habituation, improves task learning rates once rewarded trials begin. We first describe a computational model for spatial navigation inspired by rat studies. The model exploits offline replay of trajectories previously learned by applying reinforcement learning. Then, to assess our hypothesis, the model is evaluated in a “multiple T-maze” environment where rats need to learn a path from the start of the maze to the goal. Simulation results support our hypothesis that pre-exposed or habituated rats learn the task significantly faster than non-pre-exposed rats. Results also show that this effect increases with the number of pre-exposed trials. 

We present a biologically-inspired computational model of the rodent hippocampus based on recent studies of the hippocampus showing that its longitudinal axis is involved in complex spatial navigation. While both poles of the hippocampus, i.e. septal (dorsal) and temporal (ventral), encode spatial information; the septal area has traditionally been attributed more to navigation and action selection; whereas the temporal pole has been more involved with learning and motivation. In this work we hypothesize that the septal-temporal organization of the hippocampus axis also provides a multi-scale spatial representation that may be exploited during complex rodent navigation. To test this hypothesis, we developed a multi-scale model of the hippocampus evaluated it with a simulated rat on a multi-goal task, initially in a simplified environment, and then on a more complex environment where multiple obstacles are introduced. In addition to the hippocampus providing a spatial representation of the environment, the model includes an actor-critic framework for the motivated learning of the different tasks.