四足歩行ロボットの都市ナビゲーションのためのエンドツーエンド自動運転ポリシーの調整
Taming an End-to-End Autonomous Driving Policy for Urban Navigation of Quadruped Robots
自動運転の軌道計画フレームワークDrivoRを四足歩行ロボット向けに適応し、目標条件付きの都市ナビゲーションを実現した。シミュレーションで訓練し、実世界の軌道予測にゼロショット転移した。
著者: Joochan Kim, Chanuk Yang, Tackgeun You, Ziran Wang, Hwasup Lim
分類: cs.RO, cs.AI
原文アブストラクト
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.