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arXiv · 2610.04536

WasserMan: Benchmark for Underwater Manipulation Policy Learning

Danil Belov·Artem Erkhov·Sergei Parsegov·Pavel Osinenko
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Abstract

Underwater manipulation couples visual decisions, contact forces and a thruster-controlled floating base. We introduce WasserMan, to our knowledge the first multi-task simulation benchmark for visuomotor learning of floating-base underwater contact manipulation. It provides ten expert-solvable tasks, two vehicle-arm platforms, a bimanual configuration and underwater dynamics. Nine tasks have learned-policy evaluations. We compare ACT, diffusion policies (DP) and behavioral cloning on six tasks, with three training runs and equal sampled-window budgets. Pretrained SmolVLA adds evaluations on three tasks. In the tested settings, removing integral action can prevent completion, while changing action interfaces can reduce learned-policy success despite successful expert replay. Currents produce task-dependent changes in completion and actuation effort. Versioned tasks, demonstrations and per-episode evidence support a protocol separating expert feasibility, learned completion and execution effort.

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Danil Belov, Artem Erkhov, Sergei Parsegov, Pavel Osinenko. 2026-10-03. WasserMan: Benchmark for Underwater Manipulation Policy Learning. https://arxiv.org/abs/2610.04536

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