4 papers
State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-to-End Imitation Learning
Yuxiang Liu, Shengfan Cao
We study visual domain transfer for end-to-end imitation learning in a realistic and challenging setting where target-domain data are strictly off-policy, expert-free, and scarce.…
Constrained Policy Optimization via Sampling-Based Weight-Space Projection
Shengfan Cao, Francesco Borrelli, Eunhyek Joa
Safety-critical learning requires policies that improve performance without leaving the safe operating regime. We study constrained policy learning where model parameters must sati…
The Auton Agentic AI Framework
Sheng Cao, Zhao Chang, Chang Li +3
The field of Artificial Intelligence is undergoing a transition from Generative AI -- probabilistic generation of text and images -- to Agentic AI, in which autonomous systems exec…
A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing
Shengfan Cao, Eunhyek Joa, Francesco Borrelli
Guaranteeing constraint satisfaction is challenging in imitation learning (IL), particularly in tasks that require operating near a system's handling limits. Traditional IL methods…