Paper-Conference

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
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Constrained Policy Optimization via Sampling-Based Weight-Space Projection featured image
Safe Learning

Constrained Policy Optimization via Sampling-Based Weight-Space Projection

Safety-critical learning requires policies that improve performance without leaving the safe operating regime. We study constrained policy learning where model parameters must …

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Shengfan Cao
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A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing featured image
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 …

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Shengfan Cao
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