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Workshop: Generalization in Planning (GenPlan '23)

Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI

Emily Jin · Jiaheng Hu · Zhuoyi Huang · Ruohan Zhang · Jiajun Wu · Fei-Fei Li · Roberto Martín-Martín

Keywords: [ Embodied AI Benchmark ] [ Reinforcement Learning ] [ Everyday Activities ]


We present Mini-BEHAVIOR, a novel benchmark for embodied AI that challenges agents to plan and solve complex activities resembling everyday human household tasks. The Mini-BEHAVIOR environment extends the widely used MiniGrid grid world with new modes of actuation, combining navigation and manipulation actions, multiple objects, states, scenes, and activities defined in first-order logic. Mini-BEHAVIOR implements various household tasks from the original BEHAVIOR benchmark, along with starter code for data collection and reinforcement learning agent training. Together with Mini-BEHAVIOR, we also include a procedural generation mechanism to create countless variations of each task and support the study of plan generalization and open-ended learning. Mini-BEHAVIOR is fast and easy to use and extend, providing the benefits of rapid prototyping while striking a good balance between symbolic-level decision-making and physical realism, complexity, and embodiment constraints found in complex embodied AI benchmarks. Our goal with Mini-BEHAVIOR is to provide the community with a fast, easy-to-use and modify, open-ended benchmark for developing and evaluating decision-making and generalizing planning solutions for embodied AI. Code is available at

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