@inproceedings{
jin2023minibehavior,
title={Mini-{BEHAVIOR}: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied {AI}},
author={Emily Jin and Jiaheng Hu and Zhuoyi Huang and Ruohan Zhang and Jiajun Wu and Li Fei-Fei and Roberto Mart{\'\i}n-Mart{\'\i}n},
booktitle={NeurIPS 2023 Workshop on Generalization in Planning},
year={2023},
url={https://openreview.net/forum?id=Ghl9pYaVh5}
}
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 https://github.com/StanfordVL/mini_behavior.
Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI
Emily Jin Jiaheng Hu Zhuoyi Huang Ruohan Zhang Jiajun Wu Li Fei-Fei Roberto Martín-Martín