3 papers
cs.RO2026
Scaling Self-Play for End-to-End Driving
Luke Rowe, Roger Girgis, Rodrigue de Schaetzen +6
End-to-end autonomous driving models are typically trained on offline human-demonstration datasets that provide limited state coverage and often no closed-loop feedback, making the…
cs.LG2026
Constrained Group Relative Policy Optimization
Roger Girgis, Rodrigue de Schaetzen, Luke Rowe +3
While Group Relative Policy Optimization (GRPO) has emerged as a scalable framework for critic-free policy learning, extending it to settings with explicit behavioral constraints r…
cs.RO2025
Poutine: Vision-Language-Trajectory Pre-Training and Reinforcement Learning Post-Training Enable Robust End-to-End Autonomous Driving
Luke Rowe, Rodrigue de Schaetzen, Roger Girgis +2
Maintaining good driving behavior in out-of-distribution scenarios remains a critical challenge in autonomous driving. A promising direction is to leverage the generalist knowledge…