We present Mini-BEHAVIOR, a novel benchmark for embodied AI that challenges agents to use reasoning and decision-making skills to solve complex activities that resemble everyday human challenges. The Mini-BEHAVIOR environment is a fast, realistic Gridworld environment that offers the benefits of rapid prototyping and ease of use while preserving a symbolic level of physical realism and complexity found in complex embodied AI benchmarks. We introduce key features such as procedural generation, to enable the creation of countless task variations and support open-ended learning. Mini-BEHAVIOR provides implementations of various household tasks from the original BEHAVIOR benchmark, along with starter code for data collection and reinforcement learning agent training. In essence, Mini-BEHAVIOR offers a fast, open-ended benchmark for evaluating decision-making and planning solutions in embodied AI. It serves as a user-friendly entry point for research and facilitates the evaluation and development of solutions, simplifying their assessment and development while advancing the field of embodied AI. Code is publicly available at https://github.com/StanfordVL/mini_behavior.

翻译：我们提出Mini-BEHAVIOR——一个面向具身AI的新型基准测试，它挑战智能体运用推理与决策能力来解决类似日常人类挑战的复杂活动。Mini-BEHAVIOR环境是一个快速、逼真的网格世界环境，在保留复杂具身AI基准中物理真实性与复杂性的符号化层面的同时，兼具快速原型开发与易用性优势。我们引入了程序化生成等关键特性，以创建海量任务变体并支持开放式学习。Mini-BEHAVIOR提供了原始BEHAVIOR基准中各类家务任务的实现，以及数据采集和强化学习智能体训练的起始代码。本质上，Mini-BEHAVIOR为评估具身AI中的决策与规划方案提供了快速、开放的基准，它既是便于上手的研究入口点，又能促进解决方案的评估与开发，在简化评估流程的同时推动具身AI领域发展。代码已开源：https://github.com/StanfordVL/mini_behavior