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20242026
most citedTokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving

2 citations · 6 across the 17 of their papers we have counts for

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10 papers · 1 filter

cs.RO2026

Planning-aligned Token Compression for Long-Context Autonomous Driving

Zhixuan Liang, Yuxiao Chen, Yurong You +12

Monolithic vision-action models represent an emerging paradigm in autonomous driving. However, this architecture produces token sequences that quickly exceed real-time computationa…

cs.RO2026

ReSteer: Quantifying and Refining the Steerability of Multitask Robot Policies

Zhenyang Chen, Alan Tian, Liquan Wang +5

Despite strong multi-task pretraining, existing policies often exhibit poor task steerability. For example, a robot may fail to respond to a new instruction ``put the bowl in the s…

cs.RO2026

Accelerating Structured Chain-of-Thought in Autonomous Vehicles

Yi Gu, Yan Wang, Yuxiao Chen +8

Chain-of-Thought (CoT) reasoning enhances the decision-making capabilities of vision-language-action models in autonomous driving, but its autoregressive nature introduces signific…

cs.RO2025

Counterfactual VLA: Self-Reflective Vision-Language-Action Model with Adaptive Reasoning

Zhenghao "Mark" Peng, Wenhao Ding, Yurong You +11

Recent reasoning-augmented Vision-Language-Action (VLA) models have improved the interpretability of end-to-end autonomous driving by generating intermediate reasoning traces. Yet…

cs.RO2025

RoaD: Rollouts as Demonstrations for Closed-Loop Supervised Fine-Tuning of Autonomous Driving Policies

Guillermo Garcia-Cobo, Maximilian Igl, Peter Karkus +5

Autonomous driving policies are typically trained via open-loop behavior cloning of human demonstrations. However, such policies suffer from covariate shift when deployed in closed…

cs.RO2025★ 1 cited

Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail

NVIDIA, :, Yan Wang +41

End-to-end architectures trained via imitation learning have advanced autonomous driving by scaling model size and data, yet performance remains brittle in safety-critical long-tai…