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We present Mini-BEHAVIOR, a novel benchmark for embodied AI that challengesagents to use reasoning and decision-making skills to solve complex activitiesthat resemble everyday human challenges. The Mini-BEHAVIOR environment is afast, realistic Gridworld environment that offers the benefits of rapidprototyping and ease of use while preserving a symbolic level of physicalrealism and complexity found in complex embodied AI benchmarks. We introducekey features such as procedural generation, to enable the creation of countlesstask variations and support open-ended learning. Mini-BEHAVIOR providesimplementations of various household tasks from the original BEHAVIORbenchmark, along with starter code for data collection and reinforcementlearning agent training. In essence, Mini-BEHAVIOR offers a fast, open-endedbenchmark for evaluating decision-making and planning solutions in embodied AI.It serves as a user-friendly entry point for research and facilitates theevaluation and development of solutions, simplifying their assessment anddevelopment while advancing the field of embodied AI. Code is publiclyavailable at https://github.com/StanfordVL/mini_behavior.

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