IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks

By Youngwoon Lee, Edward S. Hu, Zhengyu Yang, Alexander Yin, Joseph J. Lim. 2019

154 citations recorded by the source corpus.

The IKEA Furniture Assembly Environment is one of the first benchmarks for testing and accelerating the automation of long-horizon and hierarchical manipulation tasks. The environment is designed to advance reinforcement learning and imitation learning from simple toy tasks to complex tasks requiring both long-term planning and sophisticated low-level control. Our environment features 60 furniture models, 6 robots, photorealistic rendering, and domain randomization. We evaluate reinforcement learning and imitation learning methods on the proposed environment. Our experiments show furniture assembly is a challenging task due to its long horizon and sophisticated manipulation requirements, which provides ample opportunities for future research. The environment is publicly available at https://clvrai.com/furniture.

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Citations in the corpus

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning (2020, neutral): “, 2019), IKEA (Lee et al., 2019), BabyAI (Chevalier-Boisvert et al.”
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation (2020, background): “A variety of new environments have been created recently to benchmark reinforcement learning algorithms [15, 34, 35, 36, 37].”
BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments (2021, unknown): “While benchmarks for stationary manipulation [55, 29, 28, 30, 56, 31, 32] fare well on physical realism, they commonly fall short on diversity (of scenes, objects, tasks) and complexity (e.g., simple activities that take a few seconds).”
dm_control: Software and Tasks for Continuous Control (2020, background): “More recent task suites such as Meta-world (Yu et al., 2019), SURREAL (Fan et al., 2018), RLbench (James et al., 2019) and IKEA (Lee et al., 2019), have been published in an attempt to satisfy the demand for tasks suites that facilitate the study of algorithms related to multi-scale control, multi-t”
iGibson, a Simulation Environment for Interactive Tasks in Large Realistic Scenes (2020, background): “However, existing simulation environments that combine physics simulation and robotic tasks often cater to a narrow set of tasks and include only clean, small-scale scenes [12, 13, 14, 15, 16, 17].”
Learning Adaptive Language Interfaces through Decomposition (2020, background): “Other settings might include open-ended building tasks, either in the real world (Knepper et al., 2013; Lee et al., 2019), or in virtual worlds like Minecraft (Johnson et al.”
AllenAct: A Framework for Embodied AI Research (2020, methodology): “…answering [15, 16], task completion [17], instruction following [18, 19], language grounding [20, 21], grasping [22, 23], object manipulation [24, 25], future prediction [26], and multi-agent collaboration [27, 28]; as well as using interaction as a tool to learn: environment representations…”
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning (2020, background): “Similar to our domains, the recently released IKEA furniture assembly benchmark (Lee et al., 2019b) emphasizes long-horizon reasoning, but does so in the context of an embodied construction task and provides a diﬀerent trade-oﬀ in terms of perceptual diﬃculty, motor control, and the combinatorial…”
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents (2021, background): “Leveraging physics simulators, many environments have been proposed that involve robots, fixed in place, for object manipulation tasks of varying complexity [51, 54, 16, 47, 24, 20, 76, 75, 35].”