CAREER: Learning Generalizable and Interpretable Embodied AI with Human Priors

This research aims to leverage human input to facilitate learning generalizable and interpretable AI controllers for autonomous software agents. Specifically, input from people will be incorporated into the learning framework to bias the learning in the form of both offline (before training) and online (during training) feedback. This approach is innovative in terms of three fundamental components of the learning problem agent: the environment, the agent’s representation, and the learning pipeline.

The investigator will first develop generative models to learn from images collected from people and to create diverse interactive environments for the learning agent from them. The investigator will then focus on interpreting the internal representation learned by the autonomous agent. Lastly, the investigator will incorporate feedback obtained from online interactions with people to ensure that the resulting AI is safe and aligned with human preferences.

The education objective of the project is to connect faculty and students from computer science, engineering, neuroscience, and social sciences by teaching AI as an interdisciplinary subject. The investigator will leverage the project’s interpretability framework to help students understand the AI's inner workings and use the interactive learning methods through simulation environments to create immersive educational experiences and motivate students from diverse backgrounds to learn the underlying mathematical principles between autonomous agents.

This research focuses on incorporating humans into three foundational components of learning an embodied agent: the environment, the agent representation, and the learning process: (1) Environment: The investigator will design a machine-learning model to generate and simulate diverse environments from human experiences. It will significantly improve the diversity and complexity of the training environments such that the trained agent can better generalize its acquired skills to unseen situations. (2) Agent representation: The investigator will interpret the learned representation of the embodied agent and discover interpretable motion primitives so that humans can comprehend the AI's internals and control the AI's behaviors in challenging unseen scenarios. (3) Learning process: The investigator will develop reward-free learning and adaptation methods to incorporate active human feedback, substantially improving AI alignment and safety.

The investigator will ground the research on indoor and outdoor embodied AI tasks, including autonomous driving, household robot navigation, and legged robot locomotion. This research program seamlessly integrates humans into the machine-learning loop to bring generalizable and interpretable embodied agents to real-world applications.

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.