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.

翻译：强化学习、模仿学习以及任务与运动规划已在多种机器人操作任务中展现出卓越性能。然而，在现有真实世界操作基准测试中，这些方法仅局限于学习诸如推拉或抓取放置等简单行为。为使自主机器人能实现更复杂的长期行为，我们提出聚焦真实世界的家具装配任务——这是一种需要应对当前机器人操作领域诸多挑战的复杂长时程任务。我们提出FurnitureBench，这是一个可复现的真实世界家具装配基准，旨在降低入门门槛并确保易于复现，使全球研究人员能可靠测试其算法并与先前工作进行比较。为便于使用，我们提供200+小时的预采集数据（5000+次示范）、3D打印家具模型、机器人环境搭建指南及系统化任务初始化方案。此外，我们开发了FurnitureSim——一款快速且逼真的FurnitureBench仿真器。我们通过装配任务对离线强化学习和模仿学习算法进行基准测试，结果表明亟需改进此类算法以使其能在真实世界解决我们的任务，这为未来研究提供了广阔空间。