CAREER: Improving Multi-Fingered Manipulation by Unifying Learning and Planning

职业：通过统一学习和规划来提高多指操作能力

基本信息

批准号：
1846341
负责人：
Tucker Hermans
金额：
$ 53.27万
依托单位：
University of Utah
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-03-15 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846341&HistoricalAwards=false
关键词：
CAREER Improving Multi Fingered Manipulation

项目摘要

For robots to act autonomously as assistants in one's daily life, as surrogates for humans in dangerous environments, or as workers in busy factories and processing centers, they must be able to fluently grasp and manipulate objects that they have never previously encountered. This project investigates new approaches to perform grasping and in-hand manipulation using multi-fingered robot hands. The primary novelty in this work comes from examining new ways of combining knowledge gained automatically by the robot from its own sensors with models given to the robot by its programmer. The research team will perform extensive empirical evaluation of the algorithms developed in order to quantify the benefit of these new techniques over previously proposed analytic or data-driven approaches. This will allow for fast translation of the research results into robots used in industrial and service settings. This project will conduct educational outreach activities using robot manipulation to increase and reinforce middle and high schoolers' interest in computing.For autonomous robots to operate seamlessly as assistants in human environments, they must be endowed with the ability to aptly perform dexterous manipulation. Contemporary robot hardware provides the necessary dexterity and sensitivity to perform dexterous manipulation, but algorithmic shortcomings currently cripple deployment of robust multi-fingered grasping, regrasping, and in-hand manipulation of unknown and partially modeled objects. The research goal of this project is to unify concepts from model-based planning and data-driven learning for manipulation to improve dexterous manipulation of unknown objects. The first research thrust examines adding model-based constraints to perform grasp synthesis with a learned deep network. The second research thrust evaluates the hypothesis that learning feedback policies, approximate models, and graph structures from tactile and visual sensing will improve execution of pre-planned in-hand manipulation trajectories. The investigator proposes viewing these problems as an instance of probabilistic inference. This enables the robot to directly reason over uncertain sensory observations and partial object-pose and contact-state information.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.

为了使机器人在日常生活中自主作为助手的自主行动，作为危险环境中人类的代孕或作为繁忙的工厂和加工中心的工人，他们必须能够流利地掌握和操纵以前从未遇到过的物体。该项目调查了使用多指机器人手进行抓握和手持操作的新方法。这项工作中的主要新颖性是研究机器人从自己的传感器和程序员给机器人赋予机器人的模型自动获得的知识的新方法。研究团队将对开发的算法进行广泛的经验评估，以量化这些新技术的好处，而不是先前提出的分析或数据驱动方法。这将使研究结果快速转换为工业和服务环境中使用的机器人。该项目将使用机器人操纵进行教育外展活动，以增加和增强中学和高中生对计算的兴趣。对于自主机器人作为人类环境中的助手无缝操作，必须赋予他们恰当地执行非智力操纵的能力。当代机器人硬件提供了执行灵巧操作的必要灵敏性和敏感性，但是算法的缺点目前削弱了强大的多指握把，重新填充，并操纵未知和部分建模的对象。该项目的研究目标是将概念从基于模型的计划和数据驱动的学习中统一，以改善对未知物体的灵活性操纵。第一项研究推力研究添加基于模型的约束以通过学习的深网执行掌握合成。第二项研究推动了以下假设：从触觉和视觉传感中学习反馈策略，近似模型和图形结构将改善预先计划的手中操纵轨迹的执行。研究者建议将这些问题视为概率推断的实例。这使机器人能够直接理解不确定的感官观察以及部分对象置态和联系状态信息。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Dexterous magnetic manipulation of conductive non-magnetic objects

DOI：
10.1038/s41586-021-03966-6
发表时间：
2021-10-21
期刊：
NATURE
影响因子：
64.8
作者：
Pham, Lan N.;Tabor, Griffin F.;Abbott, Jake J.
通讯作者：
Abbott, Jake J.

Planning Visual-Tactile Precision Grasps via Complementary Use of Vision and Touch

DOI：
10.1109/lra.2022.3231520
发表时间：
2022-12
期刊：
IEEE Robotics and Automation Letters
影响因子：
5.2
作者：
Martin Matak;Tucker Hermans
通讯作者：
Martin Matak;Tucker Hermans

Pick and Place Planning is Better Than Pick Planning Then Place Planning

DOI：
10.1109/lra.2024.3360892
发表时间：
2024-01
期刊：
IEEE Robotics and Automation Letters
影响因子：
5.2
作者：
M. Shanthi;Tucker Hermans
通讯作者：
M. Shanthi;Tucker Hermans

Attracting Conductive Nonmagnetic Objects With Rotating Magnetic Dipole Fields

DOI：
10.1109/lra.2022.3194878
发表时间：
2022-10
期刊：
IEEE Robotics and Automation Letters
影响因子：
5.2
作者：
Devin K. Dalton;G. Tabor;Tucker Hermans;J. Abbott
通讯作者：
Devin K. Dalton;G. Tabor;Tucker Hermans;J. Abbott

Learning Continuous 3D Reconstructions for Geometrically Aware Grasping

DOI：
10.1109/icra40945.2020.9196981
发表时间：
2019-10
期刊：
2020 IEEE International Conference on Robotics and Automation (ICRA)
影响因子：
0
作者：
Mark Van der Merwe;Qingkai Lu;Balakumar Sundaralingam;Martin Matak;Tucker Hermans
通讯作者：
Mark Van der Merwe;Qingkai Lu;Balakumar Sundaralingam;Martin Matak;Tucker Hermans