[Submitted on 23 Sep 2026]
Title:Taming an End-to-End Autonomous Driving Policy for Urban Navigation of Quadruped Robots
View a PDF of the paper titled Taming an End-to-End Autonomous Driving Policy for Urban Navigation of Quadruped Robots, by Joochan Kim and 4 other authors
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Abstract:We present Go2-DrivoR, a goal-conditioned adaptation of the end-to-end autonomous driving trajectory planning framework DrivoR for urban navigation with quadrupedal robots. By conditioning trajectory generation on a local-frame subgoal through a goal token and adapting the vehicle-centric scoring formulation, the method extends DrivoR to short-horizon goal-conditioned local planning without redesigning its core decoders. Specifically, we redefine drivable-area compliance for sidewalk-oriented navigation and reformulate the original ego progress term as goal-conditioned ego progress. Trained exclusively on TartanGround simulation data, Go2-DrivoR improves waypoint-conditioned planning performance on unseen simulation environments and transfers zero-shot to open-loop real-world trajectory prediction.
Comments: Accepted to IROS 2026 Workshop on AI Meets Autonomy
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.08812 [cs.RO]
(or arXiv:2610.08812v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.08812
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Joochan Kim [view email] [v1] Wed, 23 Sep 2026 06:55:23 UTC (14,374 KB)
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