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arXiv AI By Gengze Zhou, Yicong Hong, Jiazhao Zhang, Xunyi Zhao, Jian Zhou, Zixing Lei, Zun Wang, Chongyang Zhao, Xionghui Chen, Stephen Gould, Anton van den Hengel, Qi Wu

NavGPT-3: Harnessing Context in a Hierarchical Navigation Runtime

Read the original on arXiv AI →

NavGPT-3 is a hierarchical navigation runtime that links a large language model trained with long‑horizon reinforcement learning to a low‑latency action policy, enabling an embodied agent to reason, act, and monitor in parallel threads. The system’s OS‑like scheduler selects which thread controls the robot, allowing rapid interruption and reaction to real‑world events. With the NavGPT VLA action policy, the complete harness achieves state‑of‑the‑art performance on R2R‑CE and matches human performance on RxR‑CE, demonstrating a significant advance in autonomous navigation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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