CoralPlan: Observation Skill Selection and Execution for Underwater Robotic Inspection

Yuer Gao; Yu Zhao; Yi Cai

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arXiv · 2609.31211

CoralPlan: Observation Skill Selection and Execution for Underwater Robotic Inspection

Yuer Gao·Yu Zhao·Yi Cai

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Abstract

Underwater robotic inspection depends on acquiring views that reveal task-relevant structure. For a structurally complex coral colony, recognising the target is only the starting point: the robot must select and execute a viewing motion suited to the inspection task. We present CoralPlan, a vision-language system that selects an observation skill from a current camera image and task text supplied by an episode manifest. A shared motion interface executes orbit, patch, or survey as target-relative trajectories; the remaining plan fields provide operator guidance. Observation completion requires target keeping and primitive-specific coverage, while joint success also requires selection to match the recorded reference. We evaluate this interface in 144 simulated episodes and 36 matched simulation-hardware pairs. In a clear-water pool with external target-reference poses, hardware observation completion reaches 77.8% and joint success reaches 63.9%. The experiments identify both reference-mismatched completions and incomplete observations after a matching skill selection. These results connect observation-skill choice to measurable underwater execution outcomes and identify where task-directed acquisition succeeds or fails.

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Yuer Gao, Yu Zhao, Yi Cai. 2026-09-25. CoralPlan: Observation Skill Selection and Execution for Underwater Robotic Inspection. https://arxiv.org/abs/2609.31211

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