robotic-grasping and ggcnn
About robotic-grasping
skumra/robotic-grasping
Antipodal Robotic Grasping using GR-ConvNet. IROS 2020.
This project helps industrial automation engineers or robotics researchers equip their robotic arms with the ability to reliably pick up various objects. It takes camera images of objects within the robot's reach and identifies the best spots and angles for the robot's gripper to firmly grasp them. The output is precise instructions that guide the robot in performing effective 'antipodal' grasps, where two opposing forces hold the object securely.
About ggcnn
dougsm/ggcnn
Generative Grasping CNN from "Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach" (RSS 2018)
This project helps industrial automation specialists and robotics engineers train robotic arms to pick up objects reliably, even when items shift unexpectedly. By taking a depth image of a scene, it quickly determines the best way for a robot gripper to grasp an object, providing the quality and precise pose of potential grasps. This ensures smoother operation in dynamic environments like assembly lines or logistics hubs.
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