EFRI BRAID: Brain-inspired Algorithms for Autonomous Robots (BAAR)

University Of Texas At Austin, Austin TX

Investigators

Abstract

Autonomous robots, such as self-driving vehicles (SDVs) and household collaborative robots (Cobots), possess great potential to benefit society and meet several important national needs. Although artificial intelligence (AI) has made substantial progress, the current data/computational efficiency and adaptability of autonomous robots pale in comparison to humans in performing routine sensorimotor tasks such as driving and cooking. Enabling such autonomous robots to continually learn from experience and persistently improve their efficiency and resilience in the real world as humans do is imperative for their widespread deployments. This project aims to develop novel computational algorithms for robot autonomy with principles and insights of neurobiological learning and brain intelligence. The outcomes could make a multifaceted and transformative impact on autonomous robots such as SDVs, Cobots, and other intelligent robotic systems in manufacturing and healthcare applications that face the same challenges of computational/data inefficiency and adaptation inflexibility. The project seeks to provide a paradigm shift in autonomous robotic systems by incorporating brain-inspired intelligence throughout their fundamental and core capabilities of perception, planning, and continual learning. Using convergent engineering-science approaches, the project aims to create a fundamental and innovative framework of brain-inspired perception, learning, and planning algorithms for autonomous robots. The framework will be applied to SDVs and Cobots as two representative and complementary engineering systems through combined theoretical and empirical studies. Integrating brain-inspired innovations, the work will adapt and engineer the general brain-inspired methods and algorithms to SDVs and Cobots for experimental validation of the effectiveness in data- and energy-efficiency, adaptability, and resiliency. It is expected that the findings will not only provide a significant leap to SDVs and Cobots toward their real-world deployments, but also have a transformative impact on other intelligent robotic systems such as those in manufacturing and healthcare domains by improving their data/computation efficiency, adaptation resiliency, and intelligence interpretability. 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.