RI: Small: A Novel Framework for Informed Manipulation Planning

RI：小型：知情操纵规划的新颖框架

基本信息

批准号：
2008720
负责人：
Lydia Kavraki
金额：
$ 42.5万
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：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008720&HistoricalAwards=false
关键词：
RI Small Novel Framework Informed

项目摘要

Humans use many types of manipulation to accomplish daily tasks and easily sequence and execute pertinent actions. Robots are confined to very simple tasks that are often painstakingly broken down to an excruciating level of detail by humans who operate those robots. Today, more than ever, there is an urgent need to develop robots that reason about manipulation as seamlessly as humans do. Manipulation planning finds a sequence of actions that move the robot and the objects it interacts with from a start state to a goal state. In doing so, contacts are formed and broken, limits are imposed, and some high-level reasoning occurs:. Grasping, placing, regrasping, and rearranging objects often take place. The combination of manipulation planning with classical AI planning and formal methods will lead to the kind of behavior that is expected from a robot capable of cleaning a hospital room, performing tasks in an ICU, or assisting a recovering patient.This project advocates a novel, unifying framework for manipulation planning. It adopts a constraint-centric view, in particular using manifold constraints which define lower-dimensional subspaces, or modes, among which the robot must transition. The definition of transitions is also constraint-centric, defined by the combination of the constraints which define modes, and is only possible within the unified approach used to consider modes. The work depends on constructs from differential geometry and the use of multi-resolution search schemes. First, the project will generalize sampling-based algorithms for robot motion planning by decoupling the planning strategy from the satisfaction of constraints often found in manipulation. Second, the concept of a transition graph between modes will be defined and exploited during search to automatically find viable transitions between modes and produce manipulation plans. The work will start with a specific but general type of constraint, manifold constraints, and later expand to other types. The proposed research will investigate optimality and robustness guarantees provided by the proposed framework and will identify the limits of using constraints as a unifying construct in manipulation planning. The work will be supported by extensive experimentation in both simulation and realistic scenarios that involve several objects, cabinets, shelves, a mobile manipulator, and two stationary state-of-the-art manipulators.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.

人类使用多种类型的操纵来完成日常任务，并轻松地进行序列并执行相关的动作。机器人仅限于非常简单的任务，这些任务通常被艰苦地分解为经营这些机器人的人类的令人难以置信的细节。如今，比以往任何时候都需要开发机器人像人类一样无缝操纵的理由。操纵计划找到了一系列动作，这些操作将机器人及其从开始状态到目标状态的对象移动。这样做会形成和断裂，施加了限制，并发生了一些高级推理：。经常发生抓握，放置，重新填充和重新安排物体。操纵计划与经典AI计划和正式方法的结合将导致能够清洁医院房间，在ICU中执行任务或协助恢复患者的机器人所期望的行为。它采用了以约束为中心的视图，特别是使用定义较低维度子空间或模式的歧管约束，其中机器人必须过渡。过渡的定义也以约束为中心，由定义模式的约束的组合定义，只有在用于考虑模式的统一方法中才有可能。工作取决于差分几何形状的构造和多分辨率搜索方案的使用。首先，该项目将通过将计划策略与对操作中经常发现的约束的满意度相结合，从而将基于抽样的算法概括为机器人运动计划。其次，将在搜索过程中定义和利用模式之间的过渡图的概念，以自动找到模式之间的可行过渡和产生操纵计划。这项工作将以特定但一般类型的约束，多种约束开始，然后扩展到其他类型。拟议的研究将调查拟议框架提供的最优性和鲁棒性保证，并将确定使用约束作为操纵计划中统一结构的限制。在模拟和现实场景中进行广泛的实验将为这项工作提供支持，涉及几个对象，橱柜，货架，一个移动操纵器以及两个固定的最先进的操纵器。该奖项反映了NSF的法定任务，并通过基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被认为是值得的支持。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Object Reconfiguration with Simulation-Derived Feasible Actions

DOI：
10.1109/icra48891.2023.10160377
发表时间：
2023-02
期刊：
2023 IEEE International Conference on Robotics and Automation (ICRA)
影响因子：
0
作者：
Yiyuan Lee;Wil B. Thomason;Zachary K. Kingston;L. Kavraki
通讯作者：
Yiyuan Lee;Wil B. Thomason;Zachary K. Kingston;L. Kavraki

Robowflex: Robot Motion Planning with MoveIt Made Easy

Robowflex：使用 MoveIt 轻松进行机器人运动规划

DOI：
10.1109/iros47612.2022.9981698
发表时间：
2022
期刊：
IEEE
影响因子：
0
作者：
Kingston, Zachary;Kavraki, Lydia E.
通讯作者：
Kavraki, Lydia E.

A Sampling-based Motion Planning Framework for Complex Motor Actions

DOI：
10.1109/iros51168.2021.9636395
发表时间：
2021-09
期刊：
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
0
作者：
Shlok Sobti;Rahul Shome;Swarat Chaudhuri;L. Kavraki
通讯作者：
Shlok Sobti;Rahul Shome;Swarat Chaudhuri;L. Kavraki

Asymptotically Optimal Kinodynamic Planning Using Bundles of Edges

使用边束的渐近最优运动动力学规划

DOI：
10.1109/icra48506.2021.9560836
发表时间：
2021
期刊：
2021 IEEE International Conference on Robotics and Automation (ICRA
影响因子：
0
作者：
Shome, Rahul;Kavraki, Lydia E.
通讯作者：
Kavraki, Lydia E.

Human-Guided Motion Planning in Partially Observable Environments

部分可观测环境中的人类引导运动规划

DOI：
10.1109/icra46639.2022.9811893
发表时间：
2022
期刊：
IEEE International Conference on Robotics and Automation
影响因子：
0
作者：
Quintero-Pena, Carlos;Chamzas, Constantinos;Sun, Zhanyi;Unhelkar, Vaibhav;Kavraki, Lydia E.
通讯作者：
Kavraki, Lydia E.