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

迫切需要让今天的机器人在现实世界中运行时能够、健壮和高效。这个项目专注于近期的场景，这些场景需要复杂的长期推理，并对机器人的运动进行非平凡的约束。例如，在带有部分自动化存储和检索系统的仓库中操作的机器人，或者在自动化实验室中运行实验的机器人。机器人学中的几种方法通过对机器人如何与环境交互进行建模的明确和精心设计的规划域来应对上述场景的挑战。规划域提供了对整个世界的抽象，这对于长远规划的任务和运动规划(TAMP)方法是必不可少的，也就是计算需要许多步骤或具有非单调性质的可执行复杂计划，例如重新排列货架上的对象或为实验提取反应物。这项研究项目致力于开发可解释的TAMP方法，能够处理环境中日益增加的不确定性，同时不牺牲它们的优势，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。该项目的新颖性恰恰在于开发了允许增强TAMP方法的方法学，使其能够对不确定性和隐含模型进行推理。该项目的影响在于建立基础，使机器人能够执行复杂的任务，如打扫房屋、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，并开展外联活动，包括参加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感应和驱动中的噪音问题。因子图将以一种允许利用长期规划问题的内在结构的方式进行增强，以使不确定性下的规划变得有效，并放松假设以例如允许有效地使用学习的行动。第二个目标是更进一步地考虑病理性不确定性：当信息如此之少，以至于计划实际上存在缺口时。计划在执行时被动态修改，以填补这些差距，其中包括利用所学的技能来填补差距。第三个目标是用另一个未知和难以建模的信息来源：人类的偏好和批评来增强夯实方法。该项目解决了如何在系统的生命周期中使用和积累隐含的和学习的表示。重要的是，这项工作可以适用于任何高级规划者，包括可满足性模理论解算器和利用这些领域的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优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.