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：我们提出了Mini-BEHAVIOR，这是一个新颖的具身智能基准，旨在挑战智能体运用推理和决策技能来解决类似于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，在保留复杂具身智能基准中所具有的符号级物理真实性和复杂性的同时，提供了快速原型设计和易用性的优势。我们引入了程序化生成等关键特性，以创建无数任务变体并支持开放式学习。Mini-BEHAVIOR提供了原始BEHAVIOR基准中各种家务任务的实现，以及用于数据收集和强化学习智能体训练的起始代码。本质上，Mini-BEHAVIOR为评估具身智能中的决策与规划解决方案提供了一个快速、开放的基准。它作为一个用户友好的研究切入点，促进了解决方案的评估与开发，简化了其评估与开发过程，同时推动了具身智能领域的发展。代码已公开于https://github.com/StanfordVL/mini_behavior。