NRI: INT: COLLAB: Integrated Modeling and Learning for Robust Grasping and Dexterous Manipulation with Adaptive Hands

NRI：INT：COLLAB：利用自适应手实现稳健抓取和灵巧操作的集成建模和学习

基本信息

批准号：
1734492
负责人：
Kostas Bekris
金额：
$ 86.77万
依托单位：
Rutgers University New Brunswick
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2023-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734492&HistoricalAwards=false
关键词：
NRI INT COLLAB Integrated Modeling

项目摘要

Robots need to effectively interact with a large variety of objectsthat appear in warehouses and factories as well as homes and offices.This requires robust grasping and dexterous manipulation of everydayobjects through low cost robots and low complexity solutions.Traditionally, robots use rigid hands and analytical models for suchtasks, which often fail in the presence of even small errors. Newcompliant hands promise improved performance, while minimizingcomplexity, and increased robustness. Nevertheless, they areinherently difficult to sense and model. This project combines ideasfrom different robotics sub-fields to address this limitation. Itutilizes progress in machine learning and builds on a strong traditionin robot modeling. The objective is to provide adaptive, compliantrobots that are better in grasping objects in the presence of multipleunknown contact points and sliding or rolling objects in-hand. Thebroader impact will be strengthened by the open release of new ormodified robot hand designs, improved control algorithms and software,as well as corresponding data sets. Furthermore, academicdissemination will be accompanied by educational outreach toundergraduate and high school students.Towards the above objective, the first step will be the definition ofnew hybrid models appropriate for adaptive, compliant hands. Thiswill happen by improving analytical solutions and extending them toallow adaptation based on data via novel, time-efficient learningmethods. The objective is to capture model uncertainty inherent inreal-world interactions; a process that suffers from data scarcity.In order to reduce the amount of data required for learning, differentmodels will be tailored to specific tasks through an automateddiscovery of these tasks and of underlying motion primitives for eachone of them. This task identification process will operate iterativelywith learning and utilize improved models to discover new tasks. Itcan also provide feedback for improved hand design. Once theselearning-based and task-focused models are available, they will beused to learn and synthesize controllers for grasping and in-handmanipulation. To learn controllers, this work will consider amodel-based, reinforcement learning approach, which will be evaluatedagainst alternatives. For controller synthesis, existing tools forthis purpose will be integrated with task planning primitives andextended through learning processes to identify the preconditionsunder which different controllers can be chained together. The projectinvolves extensive evaluation on a variety of novel adaptive hands androbotic arms designed in the PIs' labs. Modern vision-based solutionswill be used to track grasped objects and provide feedback forlearning and closed-loop control. The evaluation will measure whetherthe developed hybrid models can significantly improve robustness ofgrasping and the effectiveness of dexterous manipulation.

机器人需要有效地与各种各样的物体互动，这在仓库和工厂以及房屋和办公室中都需要进行。这需要通过低成本机器人和低复杂性解决方案对日常对象进行牢固的抓握和灵活的操纵。传统上，机器人在这种情况下使用刚性的手和分析模型，这些模型通常会在较小的情况下使用，这些模型通常会在较小的情况下进行。 NewCompliant Hands承诺提高了性能，同时最大程度地增加了复杂性和稳健性。然而，它们很难感知和模型。该项目结合了不同的机器人学子场的想法来解决此限制。 it iTUTILLID在机器学习方面的进步，并以强大的传统机器人建模为基础。目的是提供自适应的，适合的，它们在存在多个互联的接触点以及手中滑动或滚动对象的情况下抓住对象的更好。新的Ormotified机器人手设计，改进的控制算法和软件以及相应的数据集将通过开放释放新的Ormotified机器人手工设计来加强影响力的影响。此外，学术授课将伴随着教育外展Tounerthergrader和高中生。符合上述目标，第一步将是适合适应性，兼容的手的New Hybrid模型的定义。这将通过改善分析解决方案并通过新颖的，及时的学习方法扩展其适应性来实现。目的是捕获模型的不确定性固有的Inreal-world交互。为了减少学习所需的数据量的为了减少数据稀缺的过程，不同模型将通过这些任务的自动发现和每个人的基本运动原始人来量身定制为特定任务。此任务识别过程将在迭代中进行学习，并利用改进的模型来发现新任务。 IT还可以提供改进的手工设计的反馈。一旦可以使用基于本的学习和以任务为中心的模型，他们将求助学习和合成控制器以掌握和掌握。为了学习控制者，这项工作将考虑基于AMODEL的强化学习方法，这将是评估替代方案。对于控制器的综合，现有的工具将与任务计划原始词集成并通过学习过程进行扩展，以确定可以将不同的控制器链接在一起的前提。 ProjectInvolves对PIS实验室中设计的各种新型自适应手雄动臂进行了广泛的评估。基于现代视觉的解决方案将用于跟踪握把的对象，并提供用于学习和闭环控制的反馈。该评估将衡量开发的杂种模型是否可以显着改善gr缩的鲁棒性和灵巧操作的有效性。

项目成果

期刊论文数量（50）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search

统一对象重排：从完整的单调基元到高效的非单调知情搜索

DOI：
发表时间：
2021
期刊：
IEEE International Conference on Robotics and Automation (ICRA
影响因子：
0
作者：
Wang, Rui;Gao, Kai;Nakhimovich, Daniel;Yu, Jingjin;Bekris, Kostas E
通讯作者：
Bekris, Kostas E

Object Rearrangement with Nested Nonprehensile Manipulation Actions

使用嵌套的不可理解的操作操作重新排列对象

DOI：
10.1109/iros40897.2019.8967548
发表时间：
2019
期刊：
Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems
影响因子：
0
作者：
Song, Changkyu;Boularias, Abdeslam
通讯作者：
Boularias, Abdeslam

Interleaving Monte Carlo Tree Search and Self-Supervised Learning for Object Retrieval in Clutter

DOI：
10.1109/icra46639.2022.9812132
发表时间：
2022-02
期刊：
2022 International Conference on Robotics and Automation (ICRA)
影响因子：
0
作者：
Baichuan Huang;Teng Guo;Abdeslam Boularias;Jingjin Yu
通讯作者：
Baichuan Huang;Teng Guo;Abdeslam Boularias;Jingjin Yu

Tools for Data-driven Modeling of Within-Hand Manipulation with Underactuated Adaptive Hands

欠驱动自适应手的手内操作数据驱动建模工具

DOI：
发表时间：
2020
期刊：
Proceedings of Machine Learning Research
影响因子：
0
作者：
Sintov, Avishai;Kimmel, Andrew;Wen, Bowen;Boularias, Abdeslam;Bekris, Kostas
通讯作者：
Bekris, Kostas

Any-axis Tensegrity Rolling via Bootstrapped Learning and Symmetry Reduction

通过引导学习和对称性降低进行任意轴张拉整体滚动

DOI：
发表时间：
2018
期刊：
International Symposium on Experimental Robotics (ISER
影响因子：
0
作者：
Surovik, David;Bruce, Jonathan;Wang, Kun;Vespignani, Massimo;Bekris, Kostas E
通讯作者：
Bekris, Kostas E