DobotTrack: Target-Grounded Vision-Language-Action Learning for Embodied Person Tracking
Overview
This paper introduces DobotTrack, a target-grounded vision-language-action (VLA) framework designed for embodied person tracking.
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Event evidence and corrections
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Reported quantity · Reported success rate: 77.9 percent · reported trials
“erve identity-discriminative appearance features and spatial cues in compact historical tokens. On EVT-Bench, our 0.8B-parameter model achieves an average success rate of 77.9%, exceeding OmTrackVLA by 16.9 percentage points across the three tasks. We further construct a real-world person-tracking dataset collected using a quadruped robot, comprising 126 episodes and 596,323 mask-annotated frames. The target masks en”
Exact source · revision 1Source owner not reported
Reported quantity · episodes: 126 other · reported trials
“erve identity-discriminative appearance features and spatial cues in compact historical tokens. On EVT-Bench, our 0.8B-parameter model achieves an average success rate of 77.9%, exceeding OmTrackVLA by 16.9 percentage points across the three tasks. We further construct a real-world person-tracking dataset collected using a quadruped robot, comprising 126 episodes and 596,323 mask-annotated frames. The target masks en”
Exact source · revision 1Source owner not reported
Reported quantity · frames: 596323 other · Basis not reported
“erve identity-discriminative appearance features and spatial cues in compact historical tokens. On EVT-Bench, our 0.8B-parameter model achieves an average success rate of 77.9%, exceeding OmTrackVLA by 16.9 percentage points across the three tasks. We further construct a real-world person-tracking dataset collected using a quadruped robot, comprising 126 episodes and 596,323 mask-annotated frames. The target masks en”
Exact source · revision 1Source owner not reported
Artifact availability · code: available
“able automatic derivation of bounding boxes and visual-spatial labels encoding horizontal position and apparent image scale, providing additional grounding supervision without separate manual annotation of these labels. Together, our framework and annotation pipeline support learning target-grounded tracking policies from simulated and real-world data. Code is available at https://github.com/Dobot-Team/dobot_track.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalDobotTrack: Target-Grounded Vision-Language-Action Learning for Embodied Person Tracking
This paper introduces DobotTrack, a target-grounded vision-language-action (VLA) framework designed for embodied person tracking.
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