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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.

人类使用许多类型的操作来完成日常任务，并轻松地排序和执行相关操作。机器人仅限于执行非常简单的任务，而操作这些机器人的人类往往会煞费苦心地将这些任务分解到极其细致的程度。今天，比以往任何时候都更迫切需要开发像人类一样无缝地推理操作的机器人。操纵规划发现了一系列动作，这些动作将机器人及其与之交互的对象从开始状态移动到目标状态。在这样做的过程中，接触形成和打破，限制被强加，一些高层次的推理发生：抓取、放置、重新抓取和重新排列物体经常发生。将操作规划与经典的人工智能规划和形式化方法相结合，将导致机器人能够清洁医院房间，在ICU执行任务或协助康复患者的预期行为。该项目倡导一种新颖的，统一的操作规划框架。它采用了以约束为中心的观点，特别是使用定义低维子空间或模式的流形约束，机器人必须在这些子空间或模式之间转换。转换的定义也是以约束为中心的，由定义模式的约束的组合来定义，并且仅在用于考虑模式的统一方法中是可能的。这项工作依赖于微分几何的结构和多分辨率搜索方案的使用。首先，该项目将推广基于采样的机器人运动规划算法，通过解耦的规划策略，从满足经常在操作中发现的约束。其次，模式之间的转换图的概念将被定义和利用在搜索过程中自动找到可行的模式之间的转换，并产生操纵计划。这项工作将从一个特定但通用的约束类型开始，即流形约束，然后扩展到其他类型。拟议的研究将调查所提出的框架提供的最优性和鲁棒性保证，并将确定使用约束作为一个统一的结构在操纵规划的限制。这项工作将得到广泛的实验支持，包括模拟和现实场景，涉及几个对象，橱柜，货架，一个移动的机械手，和两个固定的最先进的机械手。这个奖项反映了NSF的法定使命，并已被认为是值得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