5 papers
Interleaved Projected Gradient Descent for Safe Imitation Learning
Shengfan Cao, Francesco Borrelli
We propose an imitation-learning design for neural-network control policies under state and input constraints. Training alternates a standard imitation gradient step with a block o…
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…
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…
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.…
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…