Deploying robots in open-ended unstructured environments such as homes has been a long-standing research problem. However, robots are often studied only in closed-off lab settings, and prior mobile manipulation work is restricted to pick-move-place, which is arguably just the tip of the iceberg in this area. In this paper, we introduce Open-World Mobile Manipulation System, a full-stack approach to tackle realistic articulated object operation, e.g. real-world doors, cabinets, drawers, and refrigerators in open-ended unstructured environments. The robot utilizes an adaptive learning framework to initially learns from a small set of data through behavior cloning, followed by learning from online practice on novel objects that fall outside the training distribution. We also develop a low-cost mobile manipulation hardware platform capable of safe and autonomous online adaptation in unstructured environments with a cost of around 20,000 USD. In our experiments we utilize 20 articulate objects across 4 buildings in the CMU campus. With less than an hour of online learning for each object, the system is able to increase success rate from 50% of BC pre-training to 95% using online adaptation. Video results at https://open-world-mobilemanip.github.io/

翻译：将机器人部署于家庭等开放式非结构化环境是一个长期存在的研究难题。然而，机器人研究往往局限于封闭实验室环境，且现有的移动操控工作多限于"抓取-移动-放置"这类操作——这仅是冰山一角。本文提出开放世界移动操控系统(Open-World Mobile Manipulation System)，这是一种面向真实铰接物体（如现实环境中的门、橱柜、抽屉和冰箱）操作的端到端解决方案。该机器人采用自适应学习框架：首先通过行为克隆从少量数据中学习初始策略，随后针对训练分布外的未见物体进行在线实践学习。我们还开发了低成本移动操控硬件平台（约20,000美元），可在非结构化环境中实现安全自主的在线自适应。在卡内基梅隆大学四栋建筑的20个铰接物体实验中，系统对每个物体进行不到一小时的在线学习后，操作成功率从行为克隆预训练的50%提升至在线自适应后的95%。视频结果见https://open-world-mobilemanip.github.io/