2 papers
cs.RO2026
MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving
Rajeev Yasarla, Deepti Hegde, Hsin-Pai Cheng +9
Vision-language-action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings due to being trained under traditional im…
cs.CV2026
Generative Scenario Rollouts for End-to-End Autonomous Driving
Rajeev Yasarla, Deepti Hegde, Shizhong Han +10
Vision-Language-Action (VLA) models are emerging as highly effective planning models for end-to-end autonomous driving systems. However, current works mostly rely on imitation lear…