A Framework for Manipulation Planning and Execution under Uncertainty in Partially-Known Environments

部分已知环境中不确定性下的操纵规划和执行框架

• 批准号: 2336612
• 负责人: Lydia Kavraki
• 金额: $ 71.53万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2336612&HistoricalAwards=false
• 关键词: Framework; Manipulation; Planning; Execution; under

There is a pressing need to make today's robots capable, robust, and efficient during real-world operation. This project focuses on near-future scenarios that require complex long-horizon reasoning with non-trivial constraints on the robot's motion. Examples include a robot operating in a warehouse with a partly automated storage and retrieval system or a robot running experiments in an automated laboratory. Several methods in robotics address the challenges of the above scenarios with explicit and carefully crafted planning domains that model how the robot interacts with the environment. Planning domains provide an abstraction over the world that is essential for Task and Motion Planning (TAMP) methods that plan over long horizons, that is, compute executable complex plans that require many steps or have non-monotonic properties, such as rearranging objects on shelves or fetching reactants for an experiment. This research project is working to develop interpretable TAMP methods with the capability to deal with increasing uncertainty in the environment, while not sacrificing their strengths and providing a structured framework that allows for a meaningful connection with emerging model-free approaches. The project's novelties lie precisely in the development of methodologies that allow the augmentation of TAMP methods with the capability to reason about uncertainty and implicit models. The project's impact is in building the foundations that will enable robots to perform complex tasks such as cleaning a house, helping doctors and nurses, assisting elderly persons, and even performing science-related tasks in the far-off reaches of space. The team is training undergraduate, graduate and postdoctoral students, and pursuing outreach activities including participation to CRA-WP programs. On a technical front, the project supports three aims. The first aim addresses noise in the sensing and actuation of the robot. Factor graphs will be enhanced in a way that allows exploiting inherent structure in long-horizon planning problems to make planning under uncertainty efficient, and loosen assumptions to, e.g., allow efficient use of learned actions. The second aim goes further to consider pathological uncertainty: when there is so little information that there is effectively a gap in the plan. Plans are dynamically modified at execution time to fill these gaps leveraging, among others, learned skills to close gaps. The third aim focuses on augmenting TAMP methods with another source of unknown and difficult-to-model information: human preferences and critiques. The project addresses how implicit and learned representations can be used and accumulated over a system's lifetime. Importantly, the work can fit with any high-level planner, including Satisfiability Modulo Theories solvers and large language models leveraging advances in these domains.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.

迫切需要使今天的机器人在现实世界的操作中具有能力，强大和高效。这个项目的重点是在不久的将来的情况下，需要复杂的长时间的推理与非平凡的限制机器人的运动。例子包括在具有部分自动化存储和检索系统的仓库中操作的机器人或在自动化实验室中运行实验的机器人。机器人技术中的几种方法通过明确和精心制作的规划域来解决上述场景的挑战，这些规划域对机器人如何与环境进行交互进行建模。规划域提供了一个抽象的世界，这是必不可少的任务和运动规划（TAMP）的方法，计划在长期的视野，也就是说，计算可执行的复杂计划，需要许多步骤或具有非单调的属性，如重新安排货架上的对象或提取反应物的实验。该研究项目致力于开发可解释的TAMP方法，该方法具有处理环境中日益增加的不确定性的能力，同时不牺牲其优势，并提供一个结构化的框架，允许与新兴的无模型方法建立有意义的联系。该项目的新颖之处恰恰在于开发的方法，允许增强TAMP方法的能力，原因是不确定性和隐式模型。该项目的影响是建立基础，使机器人能够执行复杂的任务，如清洁房屋，帮助医生和护士，帮助老年人，甚至在遥远的太空中执行与科学有关的任务。该团队正在培训本科生、研究生和博士后学生，并开展外联活动，包括参与CRA-WP项目。 在技术方面，该项目支持三个目标。第一个目标是解决机器人的感测和致动中的噪声。因子图将以一种允许利用长期规划问题中的固有结构的方式得到增强，以使不确定性下的规划有效，并放松假设，例如，有效利用所学的知识。第二个目标进一步考虑病理不确定性：当信息如此之少，以至于计划中实际上存在差距时。计划在执行时动态修改，以填补这些差距，其中包括利用所学的技能来缩小差距。第三个目标是用另一个未知和难以建模的信息来源来增强TAMP方法：人类偏好和评论。该项目解决了如何隐式和学习表示可以使用和积累在系统的生命周期。重要的是，这项工作可以适用于任何高级规划，包括可满足性模理论求解器和利用这些领域进步的大型语言模型。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。