Yale OpenHand Project

From Yale's OpenHand Project: This project intends to establish a series of open-source hand designs, and through the contributions of the open-source user community, result in a large number of useful design modifications and variations available to researchers. Based on the original  SDM Hand , the  Model T  is the OpenHand Project's first released hand design, initially introduced at ICRA 2013. the four underactuated fingers are differentially coupled through a floating pulley tree, allowing for equal force output on all finger contacts. Based on our lab's work with iRobot and Harvard on the  iHY hand , which won the  DARPA ARM program , the  Model O  replicates the hand topology common to several commercial hands, including ones from Barrett, Robotiq, and Schunk (among others). A commercial version of this hand is currently for sale by  RightHand Robotics ... ( homepage )

Robo.Op: Opening Industrial Robotics

From MADLAB.CC: Robo.Op is an open hardware / open software platform for hacking industrial robots (IRs). Robo.Op makes it cheaper and easier to customize your IR for creative use, so you can explore the fringes of industrial robotics. The toolkit is made up of a modular physical prototyping platform, a simplified software interface, and a centralized hub for sharing knowledge, tools, and code... ( homepage ) ( github )  

VERSABALL Beer Pong Robot

From Empire Robotics: The VERSABALL is a squishy balloon membrane full of loose sub-millimeter particles. The soft ball gripper easily conforms around a wide range of target object shapes and sizes. Using a process known as “granular jamming”, air is quickly sucked out of the ball, which vacuum-packs the particles and hardens the gripper around the object to hold and lift it. The object releases when the ball is re-inflated. VERSABALL comes in multiple head shapes and sizes that use the same pneumatic base... ( Empire Robotics' site )

Robot Learning Manipulation Action Plans by "Watching" Unconstrained Videos from the World Wide Web

From Yezhou Yang, Yi Li, Cornelia Fermuller and Yiannis Aloimonos: In order to advance action generation and creation in robots beyond simple learned schemas we need computational tools that allow us to automatically interpret and represent human actions. This paper presents a system that learns manipulation action plans by processing unconstrained videos from the World Wide Web. Its goal is to robustly generate the sequence of atomic actions of seen longer actions in video in order to acquire knowledge for robots. The lower level of the system consists of two convolutional neural network (CNN) based recognition modules, one for classifying the hand grasp type and the other for object recognition. The higher level is a probabilistic manipulation action grammar based parsing module that aims at generating visual sentences for robot manipulation. The list of the grasping types. Experiments conducted on a publicly available unconstrained video dataset show that the system is able to learn manipulation actions by “watching” unconstrained videos with high accuracy.... ( article at Kurzweilai.net ) ( original paper )

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