Imitation Learning

State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-to-End Imitation Learning

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. …

y.-liu

• Dec 1, 2025 • 1 min read

Autonomous Racing

A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing

Guaranteeing constraint satisfaction is a challenging problem in imitation learning (IL), especially when the task involves operating at the handling limits of the system. While …

Shengfan Cao

• Mar 1, 2025 • 4 min read

Imitation Learning

Safe Imitation Learning at Handling Limits

Designed a constraint-aware imitation learning framework incorporating an actor-critic structure for explicit safety modeling.

Nov 1, 2024 • 1 min read

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Imitation Learning

State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-to-End Imitation Learning

A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing

Safe Imitation Learning at Handling Limits