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arXiv Computer Vision By Shengjun Deng, Yinghan Zhang, Chengrui Wei, Liu Liang, Songyuan Liu, Pengkun Zheng, Yajun Zheng, Ying Zhang, Yuan Tian, Xi Li, Teng Wang

DobotTrack: Target-Grounded Vision-Language-Action Learning for Embodied Person Tracking

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DobotTrack is a vision‑language‑action framework for embodied person tracking that uses auxiliary grounding tasks—person segmentation, bounding‑box grounding, visual‑spatial classification, future DINO feature prediction, and future‑frame prediction—to improve target delineation and localization. It introduces Q‑identity, a target‑conditioned historical visual compression module that preserves identity‑discriminative appearance features in compact tokens. On the EVT‑Bench dataset, the 0.8B‑parameter model achieves a 77.9% success rate, surpassing OmTrackVLA by 16.9 percentage points, and the authors also provide a real‑world quadruped‑robot dataset with 596,323 mask‑annotated frames for further training.

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 Computer Vision.

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