Stanford Seminar - Towards Trustworthy Autonomy - Generalizability, Safety, Embodiment

November 10, 2023 Ding Zhao of Carnegie Mellon University As AI becomes more integrated into physical autonomy, it presents a dual spectrum of opportunities and risks. In this talk, I will introduce our efforts in creating trustworthy intelligent autonomy for vital civil usage such as self-driving cars and assistant robots. In these realms, training data often exhibit significant imbalance, multi-modal complexity, and inadequacy. I will initiate the discussion by analyzing 'long-tailed' problems with rare events and their connection to safety evaluation and safe reinforcement learning. I will then discuss how modeling multi-modal uncertainties as ‘tasks’ may enhance generalizability by learning across domains. To facilitate meta-learning and continuous learning with high-dimensional inputs in vision and language, we have developed prompt-transformer structures for efficient adaptation and mitigation of catastrophic forgetting. In cases involving unknown-unknown tasks with severely limited data, we explore the potential of leveraging external knowledge from legislative sources, causal reasoning, and large language models. Lastly, we will expand intelligence development into the realm of system-level design space with meta physical robot morphologies, which may achieve generalizability and safety more effectively than relying solely on software solutions. Learn more about the speaker: https://www.meche.engineering.cmu.edu/directory/bios/zhao-ding.html