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license: cc-by-nc-4.0 |
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Robot2RobotIdentification — Dataset Description |
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Robot2RobotIdentification is a community-driven vision dataset created to help drones, UGVs, and autonomous robots detect, recognize, and understand each other in real-world environments. |
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The dataset is built from annotated frames derived from publicly available online videos (with full source attribution). |
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It provides structured labels that reflect not only what type of robotic platform appears in the scene but also how it moves and behaves. |
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As more drones, UGVs, and robots operate in our skies, streets, and industrial spaces, they contribute to safer operations, faster logistics, better inspections, stronger emergency response, and more efficient infrastructure. |
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A world with more autonomous machines is a world that works better—but only if these machines can reliably perceive one another. |
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Machine-to-machine visual awareness is quickly becoming essential for autonomy, safety, and coordination. |
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Robot2RobotIdentification supports this need by enabling models trained for: |
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- Object detection — locating aerial and ground robotic systems in diverse conditions |
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- Recognition — identifying different classes and types of robotic platforms |
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- Behavior cues — direction, speed class, maneuvers, and interaction patterns |
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- Trajectory prediction — anticipating how nearby robots or drones may move |
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- Collision avoidance and deconfliction — safe shared operation in dynamic spaces |
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Each entry includes bounding boxes, class labels, contextual metadata, and lightweight behavioral annotations that help train better perception and prediction models. |
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Because all data originates from publicly available videos, the dataset contains annotations and metadata only. |
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Users must download the actual frames directly from the original video sources and comply with all platform terms and copyright rules. |
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Robot2RobotIdentification is intended for research, simulation, autonomous navigation systems, swarm robotics, and any project where autonomous agents must reliably see and interpret each other. |
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Supported by A19Lab, Inc |
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Web: |
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https://a19lab.com |
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E-mail: |
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hello at a19lab.com |
