CRII: RI: Enabling Manipulation of Object Collections via Self-Supervised Robot Learning

CRII：RI：通过自监督机器人学习实现对象集合的操作

基本信息

批准号：
1657596
负责人：
Tucker Hermans
金额：
$ 17.5万
依托单位：
University of Utah
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：
2017-03-01 至 2019-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1657596&HistoricalAwards=false
关键词：
CRII RI Enabling Manipulation Object

项目摘要

While manipulation of individual objects in cluttered, real-world settings has received substantial attention, the problem of directly manipulating collections of objects has been left unexplored. This project investigates to what extent robots can autonomously manipulate such object collections. This project facilitates autonomous manipulation methods suitable for use in home robotic assistants. Such assistive robots stand to make a substantial impact in increasing the quality of life of older adults and persons with certain degenerative diseases. Additionally, the robot skills investigated in this project are suitable for manipulation in areas damaged in natural or man-made disasters, where building rubble and other debris need to be cleared. The project supports the development and presentation of an interactive robotics lecture for low-income school children, teaching them fundamentals of computer programming.The research goal of this project is to enable robots to manipulate and reason about groups of objects en masse. The hypothesis of this project is that treating object collections as single entities enables data-efficient, self-supervised learning of contact locations for pushing and grasping grouped objects. This project investigates a novel neural network architecture for self-supervised manipulation learning. The convolutional neural network model takes as input sensory data and a robot hand configuration. The network learns to predict as output a manipulation quality score for the given inputs. When presented with a novel scene, the robot can perform manipulation inference by evaluating the current sensory data, while directly optimizing the manipulation score predicted by the network over different hand configurations. The project supports the development of experimental protocols and the collection of associated data for dissemination to stimulate research activity in manipulation of object collections.

虽然对杂乱无章的单个对象操纵，但现实世界中的设置已引起了很大的关注，但直接操纵对象集合的问题仍未探索。该项目调查机器人可以在多大程度上可以自主操纵此类物体收集。该项目有助于适用于家庭机器人助手的自主操纵方法。这种辅助机器人将对增加某些退化性疾病的老年人和患者的生活质量产生重大影响。此外，该项目中研究的机器人技能适用于在自然或人为灾难中受损的区域的操纵，在这些区域中，需要清理建筑物瓦砾和其他碎屑。该项目支持低收入小学生的互动机器人演讲的开发和展示，教给他们计算机编程的基础。该项目的假设是，将对象集合视为单个实体可以使数据有效，自我监督的学习接触位置学习，以推动和掌握分组的对象。该项目研究了一种新型的神经网络体系结构，用于自我监督的操纵学习。卷积神经网络模型将其作为输入感觉数据和机器人手部配置。该网络学会预测给定输入的操作质量得分。当呈现一个新颖的场景时，机器人可以通过评估当前的感觉数据来执行操纵推断，同时直接优化网络预测的操作得分，而不是不同的手工配置。该项目支持开发实验方案和相关数据的收集，以刺激对象收集操作中的研究活动。