aiqbench analysis
This paper extends the DrivoR end-to-end trajectory planning framework to quadrupedal robots, enabling goal-conditioned urban navigation without redesigning core decoders. By adapting vehicle-centric scoring for sidewalk-oriented movement and training on simulation data, the method achieves zero-shot transfer to real-world open-loop prediction. The work represents a significant step in applying advanced autonomous driving policies to non-wheeled locomotion within complex environments.
Taming an End-to-End Autonomous Driving Policy for Urban Navigation of Quadruped Robots
arXiv:2610.08812v1 Announce Type: cross 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.
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.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2610.08812 [cs.RO]
(or arXiv:2610.08812v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.08812 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 189 words
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