Go2-DrivoR: End-to-End Autonomous Navigation for Quadruped Robots in Urban Environments
Taming an End-to-End Autonomous Driving Policy for Urban Navigation of Quadruped Robots
Go2-DrivoR adapts DrivoR for quadruped robots, enabling goal-conditioned local planning. Trained on simulation data, it improves waypoint planning and transfers to real-world prediction.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2610.08812v1 Announce Type: new 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.
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ocal 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 traje
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Source：arXiv Robotics — research abstracts · arxiv.org