在现实世界中打开铰接结构
Opening Articulated Structures in the Real World
AI总结：
本文以打开铰接结构为测试平台，通过13个真实场地100余次端到端测试，发现模块化系统优于端到端学习系统、感知是主要瓶颈，强调系统级研究对构建可泛化移动操控系统的重要性。
摘要：
要构建能够在未见过的环境中胜任操作未见过的物体的移动操控系统，需要具备什么条件？本研究以打开铰接结构作为移动操控测试平台来回答这一问题。具体而言，我们关注的是在没有任何特权信息的情况下该任务的端到端性能，即机器人从某个位置出发，视野中可见新颖的目标铰接物体，并且必须接近该物体并成功将其打开。我们首先为该任务开发了一个系统，然后在13个真实世界测试场地进行了100多次端到端系统测试。我们的大规模研究揭示了许多令人惊讶的发现：a) 对于该任务，模块化系统优于端到端学习系统，即使端到端学习系统在1000多次演示上进行了训练；b) 感知，而非精确的末端执行器控制，是任务成功的主要瓶颈；c) 孤立开发的最先进的铰接参数估计模型在面对以机器人为中心的视角时表现困难。总体而言，我们的发现凸显了孤立开发流水线各组件的局限性，并强调了系统级研究的必要性，为构建可泛化的移动操控系统提供了一条务实的路线图。视频、代码和模型可在项目网站上获取：https://arjung128.github.io/opening-articulated-structures/
英文摘要：
What does it take to build mobile manipulation systems that can competently operate on previously unseen objects in previously unseen environments? This work answers this question using opening of articulated structures as a mobile manipulation testbed. Specifically, our focus is on the end-to-end performance on this task without any privileged information, i.e. the robot starts at a location with the novel target articulated object in view, and has to approach the object and successfully open it. We first develop a system for this task, and then conduct 100+ end-to-end system tests across 13 real world test sites. Our large-scale study reveals a number of surprising findings: a) modular systems outperform end-to-end learned systems for this task, even when the end-to-end learned systems are trained on 1000+ demonstrations, b) perception, and not precise end-effector control, is the primary bottleneck to task success, and c) state-of-the-art articulation parameter estimation models developed in isolation struggle when faced with robot-centric viewpoints. Overall, our findings highlight the limitations of developing components of the pipeline in isolation and underscore the need for system-level research, providing a pragmatic roadmap for building generalizable mobile manipulation systems. Videos, code, and models are available on the project website: https://arjung128.github.io/opening-articulated-structures/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
