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This research addresses the critical challenge of reliable human-robot interaction (HRI) in underwater environments by optimizing gesture vocabularies and recognition logic for diver safety and efficiency. The study demonstrates that command-level processing effectively mitigates transient recognition errors, reducing unintended robot triggers during complex operations. While robotics is involved, the core contribution lies in advancing AI-driven perception and interaction frameworks specifically tailored for aquatic HRI scenarios.
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Towards Reliable Underwater Diver-Robot Interaction: Gesture Design, Interaction Logic, and Real-World Evaluation

arXiv2 min read

arXiv:2609.23392v1 Announce Type: new Abstract: Underwater human--robot interaction requires gesture commands that are both easy for divers to use and reliable for robots to recognize. We investigate these aspects through a closed-loop diver--robot interaction framework integrating a compact seven-gesture vocabulary, lightweight landmark-based recognition, and command-level interaction logic. We evaluate the framework through a user study and underwater robot experiments in a laboratory tank and a swimming pool. The user study supported the reproducibility of the gestures after brief learning. Recognition analysis further showed that visual similarity was associated with gesture confusion, while intermediate poses during gesture formation introduced temporal ambiguity. Command-level processing mitigated the effects of transient recognition errors on robot execution, reducing unintended triggers and premature task interruptions. These findings show that reliable underwater gesture interaction depends on human usability, gesture recognizability, and execution reliability in underwater interaction.

Abstract:Underwater human--robot interaction requires gesture commands that are both easy for divers to use and reliable for robots to recognize. We investigate these aspects through a closed-loop diver--robot interaction framework integrating a compact seven-gesture vocabulary, lightweight landmark-based recognition, and command-level interaction logic. We evaluate the framework through a user study and underwater robot experiments in a laboratory tank and a swimming pool. The user study supported the reproducibility of the gestures after brief learning. Recognition analysis further showed that visual similarity was associated with gesture confusion, while intermediate poses during gesture formation introduced temporal ambiguity. Command-level processing mitigated the effects of transient recognition errors on robot execution, reducing unintended triggers and premature task interruptions. These findings show that reliable underwater gesture interaction depends on human usability, gesture recognizability, and execution reliability in underwater interaction.

Robotics (cs.RO)

arXiv:2609.23392 [cs.RO]

(or arXiv:2609.23392v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.23392 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 227 words

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