ROS packages enabling the control, visualization and simulation of the Robotiq 2 Fingers Adaptive Grippers model version C3

URDF model of Robotiq 2-Finger Adaptive Robot Gripper 85

Code for our paper: "GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models"

[CoRL 2020] Fit2Form: 3D Generative Model for Robot Gripper Form Design

Unofficial RG2 Gripper URDF Model for ROS and Gazebo

MuJoCo models for Robotiq grippers

Code and models for the Crane+V2 (4DOF + gripper) with the MoveIt! Framework in ROS

S-model gripper and FT300 sensor

Schunk_PG70 and Schunk_EZN64 gripper models and communication nodes (RS232, USB)

Curated URDFs and 3D models of the robots and gripper used at airo.

A PyBullet-based simulation framework for robotic gripper grasping and data collection. It enables grasp evaluation, dataset generation, and object manipulation for AI training and automation research. The system supports custom grippers, various object models, and flexible environments.

Model for IIT's Dagana gripper

Reinforcement learning-based medication sorting task using a Panda robotic arm with a gripper. The robot is trained to grasp and place pills into compartments with distance-based rewards. Implements SAC algorithm and PyBullet for simulation, with tools for reward tracking and model evaluation.

The objective of this work was to build a robot arm with stereo vision technique to identify a standard object and do the pick and place task. We have successfully completed the estimation of position and the orientation parameters (X, Y, Z) and 𝞡y of the Center of Volume for 4 types of Standard objects, Cubes, Cuboids, Cylinders with negligible and rectifiable errors. Necessary actions were taken to minimize the errors in position and orient estimation. An embedded system was developed with user friendly software interface, and has install the vision system into the Fanuc M10 Robot ARM available in SLIIT Robotic lab. Furthermore a gripper was designed and 3D printed the prototype. It was attached to the end of the robot arm. After testing the gripper the relationship between force and voltage as well as the relationship between linear movement of the screw nut along the power screw and velocity was obtained. Moreover a graphical user interface was developed which is able to train the linear regression model, live predict the coordinates of the objects, and which can check the accuracy of the predicted data as well as which can send predicted data to the gripper and the robot arm .And In addition as a future implementation, the embedded system will be upgraded to device which can be implemented with any kind of customized Robot Arms available in the Industry.

Complete pipeline for automated/manual pick/place using UR5 with the Robotiq 2f-85 gripper and ZED stereo camera. The pipeline uses manually drawn bounding boxes or roboflow cloud models for detection of object so in theory it can be modified for pick and place of any detected object.

This is the ros package of the driver for the Weiss Robotics gripper, model IEG 76-030.

3d model of 6DOF industrial Robotic arm . Operation/Gripper - Pick and place

codes for teleoperating UR3 + robotiq 2f-140 gripper and moving VLA models.

A "Hello World" model for me to get to grips with `cog` and Replicate

Pivotal application of Grippers has rendered their performance to be efficacious, in addition to the congruous application. In this paper, we construct a mathematical model for a three-jaw gripper, to procure the fundamental design parameters that are crucial for the consideration of design constraints. A multi-objective optimization problem is formulated based on the considered design variables and constraints, which is solved to optimally obtain the dimensions of the links and dimensions of the actuating cylinder. To incorporate precision over the position of the gripper, abreast higher gripping force, statistical analysis has been adopted, solving for optimal link dimensions. By including manipulator displacement and calculating corresponding gripping force, the problem is made more real-time, that can address existing or any future gripper simulations alike. Compromised solution based on objective function formulation is applied and the obtained optimal solution is used to design three-fingered, pneumatically actuated gripper.

A template for initializing OpenXR projects with an XR rig that has snap turn and stick locomotion enabled, and has teleportation attached but disabled. Also contains Oculus hand models animated with custom animations for grip and trigger presses.

The pandemic has already taken grip over peoples’ life. Since the start of the pandemic, some countries are facing problem of ever-increasing cases. Through the data analysis of cases one can analyse how countries all over the world are doing in terms of controlling the pandemic. Analysing data leads to adapt the prevention model of the countries that are doing great in terms of lowering the graph. Predictions are made with the dataset available to the individual/country/organisations, thus helping them to decide how far they are able to control the pandemic or up to how much extent they should guide preventive measures. Through this project, a step towards helping people to understand the spread and predict the cases in their country is done. This project also gives an insight of how a country is doing in terms of limiting the spread

GRIP : The Spark Foundation ( Data Science and Business Analytics Intern ) Author :Dhivya S Prediction using Supervised ML (Level - Beginner) ● Predict the percentage of an student based on the no. of study hours. ● This is a simple linear regression task as it involves just 2 variables. ● You can use R, Python, SAS Enterprise Miner or any other tool ● Data can be found at http://bit.ly/w-data ● What will be predicted score if a student studies for 9.25 hrs/ day? ● Sample Solution : https://bit.ly/2HxiGGJ Supervised machine learningis a type of machine learning in which the machine is fed the training data which is labelled. Supervised machine learning is further categorized into regression and classification Linear regression is probably the simplest approach for statistical learning. It is a good starting point for more advanced approaches, and in fact, many fancy statistical learning techniques can be seen as an extension of linear regression. Therefore, understanding this simple model will build a good base before moving on to more complex approaches.Output type- continuous (number)

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OpenHand Project Gripper Models

Analytical modeling of FRE based gripper

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This is a training model for robotic gripper grasping detection

In this version i added a camera and a gripper in model.

3D model reconstruction fror a soft gripper using an Intel Realsense D415 sensor