# IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks

arXiv: https://arxiv.org/abs/1911.07246 · HTML: https://mlsanity.com/paper/1911.07246/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2019.

## Built on by (most cited first, 30 of 69)

- [BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments](https://mlsanity.com/paper/2108.03332/) (2021)
- [FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation](https://mlsanity.com/paper/2305.12821/) (2023)
- [HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation](https://mlsanity.com/paper/2403.10506/) (2024)
- [From Imitation to Refinement -- Residual RL for Precise Assembly](https://mlsanity.com/paper/2407.16677/) (2024)
- [JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints](https://mlsanity.com/paper/2111.12772/) (2021)
- [Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?](https://mlsanity.com/paper/2204.11134/) (2022)
- [RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies](https://mlsanity.com/paper/2506.18123/) (2025)
- [Factory: Fast Contact for Robotic Assembly](https://mlsanity.com/paper/2205.03532/) (2022)
- [Skill-based Model-based Reinforcement Learning](https://mlsanity.com/paper/2207.07560/) (2022)
- [AutoEval: Autonomous Evaluation of Generalist Robot Manipulation Policies in the Real World](https://mlsanity.com/paper/2503.24278/) (2025)
- [Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization](https://mlsanity.com/paper/2111.07999/) (2021)
- [Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors](https://mlsanity.com/paper/2205.14886/) (2022)
- [CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents](https://mlsanity.com/paper/2110.10067/) (2021)
- [DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly](https://mlsanity.com/paper/2402.19302/) (2024)
- [RL-GPT: Integrating Reinforcement Learning and Code-as-policy](https://mlsanity.com/paper/2402.19299/) (2024)
- [BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark](https://mlsanity.com/paper/2407.07788/) (2024)
- [Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly](https://mlsanity.com/paper/2309.06810/) (2023)
- [Universal Visual Decomposer: Long-Horizon Manipulation Made Easy](https://mlsanity.com/paper/2310.08581/) (2023)
- [Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation](https://mlsanity.com/paper/2510.08807/) (2025)
- [Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models](https://mlsanity.com/paper/2409.07163/) (2024)
- [Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning](https://mlsanity.com/paper/2203.13733/) (2022)
- [IKEA-Manual: Seeing Shape Assembly Step by Step](https://mlsanity.com/paper/2302.01881/) (2023)
- [Category-Level Multi-Part Multi-Joint 3D Shape Assembly](https://mlsanity.com/paper/2303.06163/) (2023)
- [Large Batch Simulation for Deep Reinforcement Learning](https://mlsanity.com/paper/2103.07013/) (2021)
- [PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection](https://mlsanity.com/paper/2212.04708/) (2022)
- [Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving](https://mlsanity.com/paper/2410.24010/) (2024)
- [RobotArena ∞: Scalable Robot Benchmarking via Real-to-Sim Translation](https://mlsanity.com/paper/2510.23571/) (2025)
- [HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers](https://mlsanity.com/paper/2205.09747/) (2022)
- [Translating a Visual LEGO Manual to a Machine-Executable Plan](https://mlsanity.com/paper/2207.12572/) (2022)
- [RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment](https://mlsanity.com/paper/2112.10143/) (2021)

## Abstract (arXiv)

The IKEA Furniture Assembly Environment is one of the first benchmarks for testing and accelerating the automation of complex manipulation tasks. The environment is designed to advance reinforcement learning from simple toy tasks to complex tasks requiring both long-term planning and sophisticated low-level control. Our environment supports over 80 different furniture models, Sawyer and Baxter robot simulation, and domain randomization. The IKEA Furniture Assembly Environment is a testbed for methods aiming to solve complex manipulation tasks. The environment is publicly available at https://clvrai.com/furniture