Go2-DrivoR: End-to-End Autonomous Navigation for Quadruped Robots
Overview
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.
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Environment: simulation
“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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- arXiv Robotics — research abstractsGo2-DrivoR: End-to-End Autonomous Navigation for Quadruped Robots in Urban Environments
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.
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