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Robotic Grasping (pick-and-place) using Faster R-CNN

In this work, we present a Faster RCNN based multi-task network that is able to simultaneously perform several tasks, objection detection, classification and angle estimation. The outputs of all three tasks are then passed through a pick-and-place robot arm system. The robot arm uses the detection, angle estimation, classification to decide a picking point, a rotated gripper angle, and a specified box, respectively. The test results show that our network achieves a mean average precision of 86.6% at IoU (intersection over union) of 0.7, and a mean accuracy of 83.5% in object detection and angle estimation, respectively. In addition, the proposed multi-task network just takes 0.072s to process one image, which is really suitable for pick-and-place robot arms.

This implementation is based on the implementation of jwyang/faster-rcnn.pytorch

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Robotic Grasping (pick-and-place) using Faster R-CNN

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  • Python 74.1%
  • Cuda 11.9%
  • C 10.2%
  • C++ 3.4%
  • Other 0.4%