Source-linked AI summary
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks
Youngwoon Lee, Edward S. Hu, Zhengyu Yang, Alex Yin, Joseph J. Lim
TL;DR
Long-horizon manipulation lacks standardized environments that combine planning, realistic simulation, and sophisticated control. The paper introduces the IKEA Furniture Assembly Environment as a benchmark with diverse furniture, robots, and research tasks, while noting simplified connection physics and fixed part identities as limitations.
Problem
Long-horizon manipulation research lacks standardized simulation environments combining hierarchical planning tasks, realistic rendering, variable agents, and dynamic domains.
Method
The paper introduces a visually realistic IKEA furniture assembly benchmark spanning perception, planning, and control with over 80 furniture models, multiple robots, and configurable attachment simulation.
Results
The resulting environment supports complex long-term manipulation research across perception, planning, and control, including domain-specific research directions such as hierarchical and model-based reinforcement learning.
Takeaways & Limitations
Furniture assembly offers a comprehensive benchmark for algorithms that must combine reliable perception, high-level planning, and sophisticated low-level control.
Takeaways & Limitations
Connections are not physically simulated as screwing; parts connect when matching connectors are sufficiently close and aligned, and identical pieces are not interchangeable.
Abstract
from arXiv · showhide
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
1 Introduction
The IKEA Furniture Assembly Environment addresses the lack of standardized simulators for long-horizon manipulation by providing a visually realistic benchmark spanning perception, planning, and control.
- It targets the shortage of standardized simulators with long-horizon hierarchical tasks, realistic rendering, variable agents, and dynamic domains.
- Furniture assembly requires selecting and ordering parts, accurately aligning attachment points, and applying force to connect them.
- The environment provides a benchmark for complex manipulation involving 3D scene understanding, step-by-step planning, and sophisticated low-level control.
- The environment supports research on perception, planning, and control, including detection, pose estimation, segmentation, scene graphs, and reinforcement-learning methods.
- It simulates over 80 furniture models, multiple robots including Baxter and Sawyer, and domain randomization across furniture, physics, lighting, and textures.
2 IKEA Furniture Assembly Environment
The environment benchmarks long-horizon furniture assembly through hierarchical task structure, realistic simulation, configurable observations and actions, and varied furniture, robots, and visual conditions.
- Environment Overview: Furniture assembly repeats selecting compatible parts, grasping them, aligning connectors, and connecting them until the model is complete.
- Assembly Simulation: The environment combines MuJoCo physics with Unity3D rendering to support fast simulation alongside configurable textures and lighting.
- Assembly Simulation: A connection is flagged when matching connector IDs satisfy configurable positional and angular thresholds, after which a connect action welds the parts.
- Furniture Models: More than 80 furniture models are represented as parts and connectors modeled from IKEA manuals, with concave parts decomposed into convex collision meshes.
- Agents: Cursor, Sawyer, and Baxter agents support configurable action and observation spaces, including state, RGB, segmentation, and depth observations.
- Domain Randomization: The environment varies furniture compositions, appearances, shapes, and physical properties to support generalization and reduce the simulation-to-reality gap.
3 Limitations and Future Work
The environment’s main limitations concern simplified and constrained attachment semantics, while future work expands physical realism, robot coverage, teleoperation, tools, collaboration, and instructional guidance.
- Limitations: Parts connect through proximity and alignment rather than physically simulated screwing, and identical pieces cannot currently substitute for predefined parts.
- Future Work: Planned extensions include additional robots, higher-DoF grippers, and 3D-device teleoperation for collecting demonstrations.
- Future Work: Future work proposes more realistic attachment mechanisms, including peg insertion, screwing, and nailing.
- Future Work: Other proposed directions include screwdriver and hammer use, multi-robot assembly, and language or visual instructions and demonstrations.
4 Related Work
Prior reinforcement-learning environments largely target games or short-term and compositional manipulation, whereas this benchmark emphasizes long-horizon furniture assembly with hierarchical structure and varied objects.
- Robotics Environments: Existing robotics environments commonly focus on short-term tasks such as picking, placing, dexterous manipulation, door opening, and peg insertion.
- Composite Tasks: Block stacking and ball serving provide composite tasks, but their objects have limited variation in shapes and physical properties.
- Benchmark Positioning: Metaworld and RLBench offer diverse tasks, but the proposed environment instead targets hierarchical, long-horizon furniture assembly across varied shapes, textures, and materials.
- Domain Randomization: Domain randomization is used to address the simulation-to-real gap through variation in textures, backgrounds, lighting, furniture models, and robots.
- Simulation Frameworks: MuJoCo was selected for fast simulation and broad reinforcement-learning use, with Unity integration enabling realistic scene and lighting variation.
5 Conclusion
The paper presents the IKEA Furniture Assembly Environment as a benchmark for complex manipulation, while identifying representation, planning, dexterous control, and domain knowledge as directions needed for complete automation.
- The IKEA Furniture Assembly Environment is a benchmark for complex manipulation requiring perception, high-level planning, and sophisticated low-level control.
- The paper presents future research directions necessary for completely automating furniture assembly.
- Representation: Real-world automation remains constrained by the difficulty of extracting object-centric representations from images when methods require object pose.
- Planning: Furniture assembly requires long-term part-ordering decisions beyond the short-horizon manipulation tasks commonly addressed by reinforcement learning.
- Dexterous Control: Dexterous control remains challenging because robots and objects have complex physical properties, while full hand control and arbitrary-object grasping are open challenges.
- Domain Knowledge: Integrating manuals, programs, and video demonstrations as domain knowledge is identified as a future direction for automated furniture assembly.