FuSe: Bio-inspired sensorimotor control for robotic locomotion with neuromorphic architectures using beyond-CMOS materials and devices

University Of Pittsburgh, Pittsburgh PA

Investigators

Abstract

In biology, a key property of mammals is the ability to move efficiently in complex environments which is enabled through neural networks called Central Pattern Generators (CPG). CPGs produce rhythmic patterns of control signals for the limbs using simple environmental cues. A prime example of CPGs in action is our own ability to navigate around obstacles. While such networks are natural from the perspective of mammals, there is currently no efficient way to engineer them using electronic devices and computers. Engineering these networks can revolutionize our ability to build future generations of robots. Agile robots that can traverse unknown and complex terrains have the potential to enable autonomous navigation for commercial transport, enhance disaster response during floods and earthquakes or to access remote and unsafe areas like malfunctioning nuclear plants or space exploration. The advances in computer engineering hardware including circuits, devices, and materials that form the core of this project will aid the creation of this new technology. The breadth of skillsets that are required to effectively train a new cadre of workforce in neuromorphic engineering for robotics makes curriculum design and integration with existing frameworks incredibly challenging. The proposed NeuRoBots educational consortium among the partnering institutions will address this issue. The main objective of this consortium is to collaborate and implement a comprehensive workforce development plan that incorporates evidence-based best practices to help train a new generation of engineers and researchers, who are equipped to satisfy the growing needs of the semiconductor industry. The goal of this award is to model, design and implement neuromorphic networks with synapses and neurons using emerging devices to achieve efficient and adaptive control in miniature robots. The inspiration comes from biological neural circuitry responsible for agile movement. The technical objectives of this project are designed to address three components: 1) The materials track focuses on physics-inspired models to understand the material and device properties to help engineer the temporal dynamics, 2) the devices track develops new devices and circuits for implementing bio-inspired neurons and synapses, and 3) the systems track will implement agile miniature robots that demonstrate bio-inspired locomotion using CPG networks and reinforcement learning. By incorporating non-linear temporal dynamics at multiple timescales through mixed-feedback control and instantiating CPG networks on scalable energy-efficient hardware built using novel devices, the target is to demonstrate a fully functional quadruped/hexapod robot that can learn to move using principles informed by neuroscience. This work can lead to transformative advances in neuromorphic computing, artificial intelligence (AI), robotics, and industrial automation, while providing deeper insights to the science of neuromodulation and self-supervised learning. Development of general-purpose neuromorphic systems that mimic the complex neuromodulatory temporal dynamics seen in neuroscience experiments offers pathways to build a new class of computing machines that address the grand challenges of the BRAIN Initiative, and advances envisioned in the CHIPS Act, benefiting the nation and society at large. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.