HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks. Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a commanded location. This is a foundational challenge for robots to be useful assistants in human environments, because it involves tackling sub-problems from across robotics: perception, language understanding, navigation, and manipulation are all essential to OVMM. In addition, integration of the solutions to these sub-problems poses its own substantial challenges. To drive research in this area, we introduce the HomeRobot OVMM benchmark, where an agent navigates household environments to grasp novel objects and place them on target receptacles. HomeRobot has two components: a simulation component, which uses a large and diverse curated object set in new, high-quality multi-room home environments; and a real-world component, providing a software stack for the low-cost Hello Robot Stretch to encourage replication of real-world experiments across labs. We implement both reinforcement learning and heuristic (model-based) baselines and show evidence of sim-to-real transfer. Our baselines achieve a 20% success rate in the real world; our experiments identify ways future research work improve performance. See videos on our website: https://ovmm.github.io/.

翻译：HomeRobot（名词）：一种经济实惠的顺应性机器人，能够在家中导航并操作多种物体，以完成日常任务。开放词汇移动操作（Open-Vocabulary Mobile Manipulation, OVMM）是指在任何未见环境中拾取任意物体并将其放置在指定位置的问题。这是机器人成为人类环境中实用助手所面临的基础性挑战，因为它涉及解决机器人学中的多个子问题：感知、语言理解、导航和操作对于OVMM都至关重要。此外，将这些子问题的解决方案整合起来也带来了重大挑战。为了推动该领域的研究，我们引入了HomeRobot OVMM基准测试，其中智能体需要在家居环境中导航，抓取新物体并将其放置在目标容器上。HomeRobot包含两个部分：仿真部分，使用大规模且多样化的精选物体集，置于高质量的多房间家居环境中；以及真实世界部分，为低成本Hello Robot Stretch提供软件栈，以鼓励各实验室重复真实世界实验。我们实现了强化学习和启发式（基于模型）基线方法，并证明了从仿真到真实世界的迁移效果。我们的基线方法在真实世界中达到了20%的成功率；实验指出了未来研究改进性能的方向。请访问我们的网站观看视频：https://ovmm.github.io/。