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

迫切需要使今天的机器人在实际操作中有能力、健壮和高效。该项目侧重于近期的场景，需要复杂的长视界推理，对机器人的运动有重要的约束。示例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设计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相互作用进行建模。规划域为任务和运动规划（Task and Motion Planning， TAMP）方法提供了一种抽象，这种抽象对于长期规划至关重要，也就是说，计算需要许多步骤或具有非单调特性的可执行复杂计划，例如重新排列架子上的对象或为实验提取反应物。该研究项目致力于开发可解释的TAMP方法，使其能够处理环境中不断增加的不确定性，同时不牺牲其优势，并提供一个结构化框架，允许与新兴的无模型方法进行有意义的连接。该项目的新颖之处恰恰在于开发了一种方法，这种方法允许增强对不确定性和隐式模型进行推理的能力。该项目的影响在于为机器人完成复杂任务奠定基础，如打扫房间、帮助医生和护士、帮助老年人，甚至在遥远的太空执行与科学相关的任务。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感知和驱动中的噪声问题。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，从而使不确定性下的规划有效，并放松假设，例如，允许有效地使用学习行为。第二个目标进一步考虑了病态不确定性：当信息太少，以至于计划实际上存在空白时。计划在执行时被动态修改，以填补这些差距，其中包括利用学习的技能来缩小差距。第三个目标侧重于用另一个未知和难以建模的信息来源：人类偏好和批评来增强TAMP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式和学习表征。重要的是，该工作可以适用于任何高级计划器，包括Satisfiability Modulo Theories解算器和利用这些领域进展的大型语言模型。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.