Interleaved Projected Gradient Descent for Safe Imitation Learning
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 …
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
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 …
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 …
Real-Time Regulation-Aware Game-Theoretic Motion Planning for Head-to-Head Autonomous Racing
This paper presents the real-time implementation and experimental validation of a regulation-aware, game-theoretic motion planning framework for autonomous racing. The framework …
f.-prignoli