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

arXiv: https://arxiv.org/abs/2310.01824 · HTML: https://mlsanity.com/paper/2310.01824/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 3 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [VirtualHome: Simulating Household Activities via Programs](https://mlsanity.com/paper/1806.07011/) (2018)
- [ProcTHOR: Large-Scale Embodied AI Using Procedural Generation](https://mlsanity.com/paper/2206.06994/) (2022)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks](https://mlsanity.com/paper/2306.13831/) (2023)
- [iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks](https://mlsanity.com/paper/2108.03272/) (2021)
- [BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments](https://mlsanity.com/paper/2108.03332/) (2021)
- [Rearrangement: A Challenge for Embodied AI](https://mlsanity.com/paper/2011.01975/) (2020)
- [Sim-to-Real Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/1806.07851/) (2018)
- [The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI](https://mlsanity.com/paper/2103.14025/) (2021)
- [HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators](https://mlsanity.com/paper/1910.11432/) (2019)
- [Griddly: A platform for AI research in games](https://mlsanity.com/paper/2011.06363/) (2020)
- [Causal Policy Gradient for Whole-Body Mobile Manipulation](https://mlsanity.com/paper/2305.04866/) (2023)
- [Modeling Dynamic Environments with Scene Graph Memory](https://mlsanity.com/paper/2305.17537/) (2023)
- [MazeBase: A Sandbox for Learning from Games](https://mlsanity.com/paper/1511.07401/) (2015)

## Built on by (most cited first, 23 of 23)

- [Visual Planning: Let's Think Only with Images](https://mlsanity.com/paper/2505.11409/) (2025)
- [FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning](https://mlsanity.com/paper/2409.16578/) (2024)
- [Are Video Models Ready as Zero-Shot Reasoners? An Empirical Study with the MME-CoF Benchmark](https://mlsanity.com/paper/2510.26802/) (2025)
- [MCU: An Evaluation Framework for Open-Ended Game Agents](https://mlsanity.com/paper/2310.08367/) (2023)
- [P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task](https://mlsanity.com/paper/2409.11279/) (2024)
- [ET-Plan-Bench: Embodied Task-level Planning Benchmark Towards Spatial-Temporal Cognition with Foundation Models](https://mlsanity.com/paper/2410.14682/) (2024)
- [SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions](https://mlsanity.com/paper/2410.18416/) (2024)
- [ELDEN: Exploration via Local Dependencies](https://mlsanity.com/paper/2310.08702/) (2023)
- [MARPLE: A Benchmark for Long-Horizon Inference](https://mlsanity.com/paper/2410.01926/) (2024)
- [Simulation to Rules: A Dual-VLM Framework for Formal Visual Planning](https://mlsanity.com/paper/2510.03182/) (2025)
- [MME-CoF-Pro: Evaluating Reasoning Coherence in Video Generative Models with Text and Visual Hints](https://mlsanity.com/paper/2603.20194/) (2026)
- [WorldAPIs: The World Is Worth How Many APIs? A Thought Experiment](https://mlsanity.com/paper/2407.07778/) (2024)
- [Framing the Game: How Context Shapes LLM Decision-Making](https://mlsanity.com/paper/2503.04840/) (2025)
- [ManiTaskGen: A Comprehensive Task Generator for Benchmarking and Improving Vision-Language Agents on Embodied Decision-Making](https://mlsanity.com/paper/2505.20726/) (2025)
- [Preference Goal Tuning: Post-Training as Latent Control for Frozen Policies](https://mlsanity.com/paper/2412.02125/) (2024)
- [Learning for Long-Horizon Planning via Neuro-Symbolic Abductive Imitation](https://mlsanity.com/paper/2411.18201/) (2024)
- [Scalable Environments Drive Generalizable Agents](https://mlsanity.com/paper/2605.18181/) (2026)
- [Learning Visual Spatial Planning from Symbolic State via Modality-Gap-Aware Self-Distillation](https://mlsanity.com/paper/2606.06076/) (2026)
- [Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation](https://mlsanity.com/paper/2508.05535/) (2025)
- [Mini-BEHAVIOR-Gran: Revealing U-Shaped Effects of Instruction Granularity on Language-Guided Embodied Agents](https://mlsanity.com/paper/2604.17019/) (2026)
- [Spectral-Progressive Thought Flow for Lightweight Multimodal Reasoning](https://mlsanity.com/paper/2606.02842/) (2026)
- [VIVA+: Human-Centered Situational Decision-Making](https://mlsanity.com/paper/2509.23698/) (2025)
- [Resolving State-Representation Mismatch: State-Space Visual Reasoning for Open-Loop VLA Planning](https://mlsanity.com/paper/2609.33412/) (2026)

## Abstract (arXiv)

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.