FRR: Semi-Structured, Under-Specified, Partially-Observable Robotic Rearrangement

FRR：半结构化、未指定、部分可观察的机器人重排

• 批准号: 2309866
• 负责人: Kostas Bekris
• 金额: $ 69.95万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309866&HistoricalAwards=false
• 关键词: FRR; Semi; Structured; Under; Specified

The project aims to develop advanced technologies for intelligent robots to efficiently and autonomously interact with objects in everyday, human environments, such as homes and grocery stores, given general, natural language task descriptions. This technology addresses significant societal issues, including the support of older adults in independent living. As people age, reduced mobility often leads to frequent and severe injuries due to impaired vision, home hazards, and weakness. Household robots can assist with tasks like retrieving, transferring, and rearranging items, such as setting up a dinner table or grabbing a jar from the back of a cabinet. Similarly, rearranging robots can assist with labor-intensive, repetitive inventory management tasks in retail operations. Such tasks, like tidying and restocking shelves, are labor-intensive and can lead to injuries, while these jobs are often difficult to fill and have high turnover rates.Reliably performing these object manipulation tasks in human, semi-structured environments involves significant uncertainty and remains challenging for modern robotics. Furthermore, new objects are frequently introduced and manipulated in semi-structured environments, such as modern homes or grocery stores, further complicating the task for robots. In particular, autonomous robots face multiple hurdles in solving manipulation tasks in these scenarios, including (1) a robot must derive a complete manipulation plan from implicit task specifications given by non-expert humans, (2) the robot must achieve accurate scene understanding in environments where prior knowledge of objects is not always available, and (3) the planning process must respect realistic partial observability constraints, where sensors like RGB-D cameras can only inspect portions of a scene at a time. To address the limitations of the state-of-the-art, the project will develop a novel Iterative Scene Understanding and Rearrangement Planning framework. The framework will build increasingly accurate models of a robot's environment progressively. The adaptive scene representation will contain the identities, geometries, and possible locations of partially observed objects, to a level sufficient for safely and effectively resolving human-assigned tasks. This representation will be leveraged to efficiently execute manipulation tasks provided by people as natural language commands under realistic visibility constraints. The project will also lay the groundwork for efficient implementations of this framework, aiming to deliver natural, high-quality solutions that achieve desirable guarantees, such as safety, resolution completeness, and solution optimality.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在为智能机器人开发先进的技术，以在鉴于一般的自然语言任务描述，以便在日常的，人类环境（例如房屋和杂货店）中与物体（例如房屋和杂货店）中的物体进行自主互动。该技术解决了重大的社会问题，包括在独立生活中的老年人的支持。随着人们的年龄，降低的流动性通常会导致视力，家庭危害和弱点造成的频繁和严重伤害。家用机器人可以协助检索，转移和重新安排物品等任务，例如设置餐桌或从橱柜后面抓住罐子。同样，重新安排的机器人可以在零售业务中协助劳动密集型，重复性的库存管理任务。这样的任务，例如整理和补货货架，是劳动密集型的，可能会导致受伤，而这些工作通常很难填补并具有很高的营业率。在人类，半结构化环境中执行这些对象操纵任务涉及严重的不确定性，并涉及现代机器人技术的挑战。此外，在半结构化环境（例如现代房屋或杂货店）中经常引入和操纵新对象，从而使机器人的任务变得更加复杂。特别是，自主机器人在解决这些情况下解决操纵任务时面临多个障碍，包括（1）机器人必须从非专家人体给予的隐式任务规范中得出完整的操纵计划一次场景的部分。为了解决最新的局限性，该项目将开发出一种新颖的迭代场景理解和重新安排计划框架。该框架将逐步建立越来越精确的机器人环境模型。 自适应场景表示形式将包含部分观察到的对象的身份，几何形状和可能的位置，达到足以安全有效地解决人类分配的任务的水平。该表示形式将被利用以有效执行人们在现实的可见性约束下作为自然语言命令提供的操纵任务。该项目还将为该框架的有效实施奠定基础，旨在提供自然，高质量的解决方案，以实现可取的保证，例如安全性，解决方案完整性和解决方案最佳性。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响来评估的支持，并被认为是值得的。