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cs.RO2026
CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving
Yunxiao Shi, Hong Cai, Mohammad Ghavamzadeh +1
End-to-end autonomous driving (E2E-AD) aims to directly map raw sensor information to driving actions. Recently, with the rapid advancement of multi-modal large language models (ML…
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…