This Repository is the complete workspace of a project that realizes force-controlled object manipulation with a dual-arm-robot (UR5 and UR10)

This repository contains ROS2 packages for controlling a UR5 robot with a Robotiq gripper, facilitating precise pick and place operations using MoveIt and providing a comprehensive toolkit for ROS2-based UR5 manipulation.

A custom PyBullet environment for UR5 pick-and-place task. Trained using the SAC algorithm from Stable-Baselines3 for continuous control and robotic manipulation.

SPARC is an intelligent ROS2 Humble-based pick-and-place robotic system integrating MoveIt, YOLOv8-OBB, and Isaac Sim. It enables real-time object detection, motion planning for Franka Panda and UR5 arms, and natural language control via an Ollama-powered chatbot—making robotic manipulation smart, interactive, and human-friendly.

This is a dual-arm manipulation project with two UR5.

This repository contains a ROS package for using the PHANToM Omni to control the UR5 robotic arm in the simulated MoveIt! environment. It further allows the user to collect demonstrations of robotic manipulation around clutter, with point cloud scans of a tree displayed in simulation to model obstacles. Lastly, the code enables the validation of different path planning methods for solving the manipulation problem.

The objective of this project is to use the UR5 robot to perform the classic pick-and-place operation automatically. I implement a program that allows you to first “teach” the UR5 the position of the start and target locations and then automatically complete the pick-and-place operation using a gripper and several different types of control.

The goal of the project is to implement robotic agents that could rapidly build structures from random objects in a disaster/crisis situation to facilitate operation of other robotic agents, or fast and affordable accommodation of the injured groups, e.g. to facilitate operation of rescue agents in a post-earthquake situation. We are proposing a system that employs robotics manipulators (UR5 industrial arm equipped with a gripper in our experiments) to stack objects with arbitrary shapes to construct a rigid wall. Some of the challenges in this problem are: 1) Planning the optimal stacking order and relative placing of the objects 2) Reliable grasping and transporting of arbitrarily shaped objects 3) Accurate detection and localization of the arm and the objects The system is being implemented over ROS to connect to and control the UR5 arm, and MoveIt package is employed to perform collision aware trajectory planning for the manipulator/grasper set. A variety of objects with different shapes and features, e.g. deformability, are considered for the experiments.

roposes an efficient grasp-to-place strategy for robotic manipulation in sparse environments using a deep Q-network. Requires less computation and time, generalizes well across scenarios, and achieves optimal grasp points via trial-and-error learning. Demonstrated effectiveness with a UR5 robotic arm and Parallel-jaw gripper.

Sense–Plan–Code–Act (SPCA) framework — LLM+PDDL planning, code generation and ROS2/UR5 simulation for embodied robotic manipulation.

A repository to facilitate setting up a Husky robot with a UR5e arm and Robotiq gripper in Gazebo.

A real world experiment robot manipulation code for Embodied Instruction Following. Only for UR5 robotic platform.

MuJoCo manipulation sim: UR5 arm with Robotiq 2F85 gripper

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ROS 2 Jazzy and MoveIt 2 based UR5 robotic arm project featuring pick-and-place motion planning, manipulation, and simulation.

A robotics project combining the UR5 manipulator, Husky mobile base, and a gripper to perform mobile manipulation using QP-based motion planning and onboard sensing.

A project for the control and planning of a BallBot equipped with a UR5e manipulator. Implements robust and model based control strategies (LQR-PI, MRAC) for stabilization and uses HTN planning and MoveIt for pick & place tasks (ROS2 - > Matlab bagfile)

This repository contains a ROS-based solution for vision-guided pick-and-place tasks using a Universal Robots UR5 robotic arm equipped with a Robotiq 85 2-finger gripper. The project integrates computer vision with robotic manipulation to enable colour based object sorting.

Developed a wearable exoskeleton to teleoperate a UR5 robot with gripper using master-slave control. Exoskeleton joint sensors map user movements in real-time to the robot simulated in Gazebo via ROS, enabling precise, intuitive bi-manual control for remote manipulation in industrial and hazardous environments.

This a task for applying to an internship at Han's Robot

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UR5e-Kitchen-Manipulation

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A project for the control and planning of a BallBot equipped with a UR5e manipulator. Implements robust and model based control strategies (LQR-PI, MRAC) for stabilization and uses HTN planning and MoveIt for pick & place tasks (ROS2 - > Matlab bagfile)

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Husky-UR5 Mobile Manipulation – ROS 2 Jazzy + Gazebo Harmonic simulation

Deep Reinforcement Learning (PPO & TD3) for UR5 robotic arm manipulation using MuJoCo, Gymnasium, and CleanRL.