DuoCook: An Overcooked-Inspired Benchmark for Visual Planning and Coordination

Coordinating two players in a cooking task requires a vision-language agent to track changing scene states, sequence recipe steps, and allocate shared resources under a time budget. We introduce an Overcooked-inspired benchmark for evaluating these capabilities in Unreal Engine environments constructed using coding agents. A single vision-language model controls both players from top-down visual observations, selecting high-level actions while the engine executes navigation. The benchmark contains 20 maps in five categories: basic, shared equipment, dynamic, dynamic with shared equipment, and separated workspaces requiring item transfers. These categories support evaluation of centralized coordination under equipment contention, changing terrain, and handoff dependencies. A prop legend and pot ingredient-count indicators provide explicit visual cues, while grid-based destinations support spatial grounding. The evaluation compares four VLMs on 16 of the 20 maps, measuring task score, order completion, delivery precision, action acceptance rate, and model API expenditure. A memory ablation on 10 of these maps examines the effect of retaining recent action history while keeping the other inputs fixed. Together, the environment design and evaluation protocol provide a framework for investigating how visual state tracking and task scheduling contribute to coordinated cooking behavior.