Robust Snake Robot Control Via A Spiking Neuron Central Pattern Generator

Snakes, due to their structure, are very well adapted to navigating small spaces and diverse, unstructured, and potentially amphibious terrain. In robotics, navigating these environments is difficult for conventional tracked, wheeled and legged robots, but snake robots should be well suited for it because of their connection to their biological analog. However, designing a robot to match the morphology of a snake comes with its own challenges: a high number of degrees of freedom and complex dynamics. Coordinating these many degrees of freedom to produce locomotion is challenging. Traditional control methods using models, sine waves or shapes fall short when applied to multiple environments and can be susceptible to process and sensor noise. However, neuroscience might be the key to coordinating these high degrees of freedom. In animals, their nervous system can somehow synchronize movements while being very robust across all types of environments. In particular, central pattern generators (CPGs) are a type of neural circuit found in many animals that produce rhythmic outputs for locomotion. Simulations of CPGs have been used in the past to control legged and hyper-redundant robots, but often require the tuning of a large number of parameters. In this work, we implement a neuron-based spiking CPG (SCPG) to control a snake robot in simulation. We generate our neural network from the ground up, fixing continuous parameters and optimizing over the discrete structure space. We compare our method to state of the art locomotion algorithms in environments of increasing complexity, and show that our SCPG has an increased robustness to environmental parameters

Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence

Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense and commercial applications. We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron and synapse connectivity, to improve machine learning approaches. We have pursued different approaches within this pipeline structure. First, as a demonstration of data-driven discovery, the team has developed a technique for discovery of repeated subcircuits, or motifs. These were incorporated into a neural architecture search approach to evolve network architectures. Second, we have conducted analysis of the heading direction circuit in the fruit fly, which performs fusion of visual and angular velocity features, to explore augmenting existing computational models with new insight. Our team discovered a novel pattern of connectivity, implemented a new model, and demonstrated sensor fusion on a robotic platform. Third, the team analyzed circuitry for memory formation in the fruit fly connectome, enabling the design of a novel generative replay approach. Finally, the team has begun analysis of connectivity in mammalian cortex to explore potential improvements to transformer networks. These constraints increased network robustness on the most challenging examples in the CIFAR-10-C computer vision robustness benchmark task, while reducing learnable attention parameters by over an order of magnitude. Taken together, these results demonstrate multiple potential approaches to utilize insight from neural systems for developing robust and efficient machine learning techniques.Comment: 11 pages, 4 figure

Robust Snake Robot Control Via A Spiking Neuron Central Pattern Generator

Snakes, due to their structure, are very well adapted to navigating small spaces and diverse, unstructured, and potentially amphibious terrain. In robotics, navigating these environments is difficult for conventional tracked, wheeled and legged robots, but snake robots should be well suited for it because of their connection to their biological analog. However, designing a robot to match the morphology of a snake comes with its own challenges: a high number of degrees of freedom and complex dynamics. Coordinating these many degrees of freedom to produce locomotion is challenging. Traditional control methods using models, sine waves or shapes fall short when applied to multiple environments and can be susceptible to process and sensor noise. However, neuroscience might be the key to coordinating these high degrees of freedom. In animals, their nervous system can somehow synchronize movements while being very robust across all types of environments. In particular, central pattern generators (CPGs) are a type of neural circuit found in many animals that produce rhythmic outputs for locomotion. Simulations of CPGs have been used in the past to control legged and hyper-redundant robots, but often require the tuning of a large number of parameters. In this work, we implement a neuron-based spiking CPG (SCPG) to control a snake robot in simulation. We generate our neural network from the ground up, fixing continuous parameters and optimizing over the discrete structure space. We compare our method to state of the art locomotion algorithms in environments of increasing complexity, and show that our SCPG has an increased robustness to environmental parameters