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  • A new “particle simulator” developed by MIT researchers improves robots’ abilities to mold materials into simulated target shapes and interact with solid objects and liquids. This could give robots a refined touch for industrial applications or for personal robotics— such as shaping clay or rolling sticky sushi rice.

    A new “particle simulator” developed by MIT researchers improves robots’ abilities to mold materials into simulated target shapes and interact with solid objects and liquids. This could give robots a refined touch for industrial applications or for personal robotics— such as shaping clay or rolling sticky sushi rice.

    Courtesy of the researchers

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Giving robots a better feel for object manipulation

A new “particle simulator” developed by MIT researchers improves robots’ abilities to mold materials into simulated target shapes and interact with solid objects and liquids. This could give robots a refined touch for industrial applications or for personal robotics— such as shaping clay or rolling sticky sushi rice.

Model improves a robot’s ability to mold materials into shapes and interact with liquids and solid objects. Watch Video


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Abby Abazorius
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由澳门太阳城最新网站的研究人员开发出一种新的学习系统提高机器人的能力,模具材料为目标的形状,并就与固体异物和液体相互作用的预测。系统,被称为学习型颗粒模拟器,可以给工业机器人更精致触摸 - 它可以在个人机器人有趣的应用程序,如橡皮泥的形状或滚动糯米寿司。

在机器人的规划,物理模拟器,捕捉不同的材料如何应对力模型。机器人被“训练”使用模型,预测其使用对象,如推实心方框或戳变形粘土相互作用的结果。但传统的基于学习型模拟器主要集中在刚性物体和无法处理的液体或柔软的物品。一些更精确的基于物理学的仿真器可以处理不同的材料,但在很大程度上依赖于机器人时与在现实世界中物体相互作用是引入误差近似技术。

在一份文件中,在上月表示学习的国际会议被提出,研究人员描述了学会如何捕捉不同材料的一小部分的新模式 - 当他们戳又刺的互动 - “颗粒”。该模型从在其中运动的基本物理是不确定的或未知的情况下,数据直接获悉。然后机器人可以使用该模型作为指导来预测液体,以及刚性和可变形材料,将其触摸的力作出反应。作为机器人控制的对象,该模型还有助于进一步完善机器人的控制。

In experiments, a robotic hand with two fingers, called “RiceGrip,” accurately shaped a deformable foam to a desired configuration — such as a “T” shape — that serves as a proxy for sushi rice. In short, the researchers’ model serves as a type of “intuitive physics” brain that robots can leverage to reconstruct three-dimensional objects somewhat similarly to how humans do.

Humans have an intuitive physics model in our heads, where we can imagine how an object will behave if we push or squeeze it. Based on this intuitive model, humans can accomplish amazing manipulation tasks that are far beyond the reach of current robots,” says first author Yunzhu Li, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL).We want to build this type of intuitive model for robots to enable them to do what humans can do.”

“When children are 5 months old, they already have different expectations for solids and liquids,” adds co-author Jiajun Wu, a CSAIL graduate student. “That’s something we know at an early age, so maybe that’s something we should try to model for robots.”

在纸张上加入李和吴有:拉斯tedrake,一个CSAIL研究员,电气工程和计算机科学(EECS)部门的教授;约书亚·特南鲍姆,脑与认知科学和CSAIL的成员,该中心的大脑,心灵系的教授,和机器(CBMM);和安东尼奥托拉尔瓦,在EECS教授,主任澳门太阳城最新网站,IBM沃森的人工智能实验室。

Dynamic graphs

该模型背后的关键创新,称为“颗粒相互作用网络”(DPI-网)中,创建动态相互作用的曲线图,其由数千节点和边,可以捕获所谓的颗粒的复杂的行为。在图中,每个节点代表的粒子。相邻节点彼此使用针对边缘,其表示使从一个颗粒至另一个的相互作用连接。在模拟器,颗粒是数百小球体的组合以弥补一些液体或可变形的对象。

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The model then leverages a “propagation” technique, which instantaneously spreads a signal throughout the graph. The researchers customized the technique for each type of material — rigid, deformable, and liquid — to shoot a signal that predicts particles positions at certain incremental time steps. At each step, it moves and reconnects particles, if needed.

例如,如果固体框被按下时,扰动颗粒将被向前移动。因为箱内所有粒子都被刚性地相互连接,在对象中的所有其它颗粒移动相同的计算出的距离,旋转,以及任何其他尺寸。颗粒的连接保持不变,并在框移动作为单个单元。但如果变形泡沫的区域缩进,效果会不同。扰动粒子向前推进了很多,周围的粒子向前幅度很小,和颗粒远不会在所有的移动。用液体被在一个杯子周围烂醉,颗粒可以完全从图形到另一个的一端跳。图形必须学会预测的位置和程度所有受影响的粒子移动,这是计算复杂。

Shaping and adapting

在他们的论文中,研究人员通过与夹紧目标的形状进行变形泡沫的任务的两指ricegrip机器人演示模型。机器人首先使用深度感应摄像机和对象识别技术来识别泡沫。研究人员随机地选择所感知的形状内部颗粒初始化粒子的位置。然后,该模型颗粒之间增加了边缘和重构泡沫成定制变形材料动态图。

因为了解到仿真,机器人已经具备了每个触摸,给予一定量的力,将影响到每个图形中的颗粒的一个好主意。随着机器人开始缩进的泡沫,它迭代匹配的颗粒与颗粒的目标位置的真实世界位置。每当颗粒不对齐时,它发送一个误差信号到模型。该信号调整模型以更好地匹配材料的真实世界的物理。

Next, the researchers aim to improve the model to help robots better predict interactions with partially observable scenarios, such as knowing how a pile of boxes will move when pushed, even if only the boxes at the surface are visible and most of the other boxes are hidden.

研究人员也在探索如何直接在图像操作模式与终端到终端的感知模块相结合。这将是与丹yamins小组的联合项目;亚明最近完成了他的博士后在澳门太阳城最新网站,目前是斯坦福大学的助理教授。 “你在处理这些案件全部那里是只有部分信息的时间,”吴先生说。 “我们正在扩大我们的模型,了解所有粒子的动态,而只看到一小部分。”


Topics: Research, Computer science and technology, Algorithms, Machine learning, Robots, Robotics, Computer modeling, Data, MIT-IBM Watson AI Lab, Computer Science and Artificial Intelligence Laboratory (CSAIL), Brain and cognitive sciences, Electrical Engineering & Computer Science (eecs), School of Engineering, School of Science

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