  Reinforcement learning (RL), imitation learning (IL), and task and motion
planning (TAMP) have demonstrated impressive performance across various robotic
manipulation tasks. However, these approaches have been limited to learning
simple behaviors in current real-world manipulation benchmarks, such as pushing
or pick-and-place. To enable more complex, long-horizon behaviors of an
autonomous robot, we propose to focus on real-world furniture assembly, a
complex, long-horizon robot manipulation task that requires addressing many
current robotic manipulation challenges to solve. We present FurnitureBench, a
reproducible real-world furniture assembly benchmark aimed at providing a low
barrier for entry and being easily reproducible, so that researchers across the
world can reliably test their algorithms and compare them against prior work.
For ease of use, we provide 200+ hours of pre-collected data (5000+
demonstrations), 3D printable furniture models, a robotic environment setup
guide, and systematic task initialization. Furthermore, we provide
FurnitureSim, a fast and realistic simulator of FurnitureBench. We benchmark
the performance of offline RL and IL algorithms on our assembly tasks and
demonstrate the need to improve such algorithms to be able to solve our tasks
in the real world, providing ample opportunities for future research.
