Year 2022

• Autonomous Perception and Grasp Generation Based on Multiple 3D Sensors and Deep Learning

  Publication

  • Z. Kowalczuk
  • J. Glinko

  - Year 2022

  Grasping objects and manipulating them is the main way the robot interacts with its environment. However, for robots to operate in a dynamic environment, a system for determining the gripping position for objects in the scene is also required. For this purpose, neural networks segmenting the point cloud are usually applied. However, training such networks is very complex and their results are unsatisfactory. Therefore, we propose...

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• Autonomous pick-and-place system based on multiple 3Dsensors and deep learning

  Publication

  • Z. Kowalczuk
  • J. Glinko

  - Year 2022

  Grasping objects and manipulating them is the main way the robot interacts with its environment. However, for robots to operate in a dynamic environment, a system for determining the gripping position for objects in the scene is also required. For this purpose, neural networks segmenting the point cloud are usually applied. However, training such networks is very complex and their results are unsatisfactory. Therefore, we propose...

  Full text to download in external service

• Training of Deep Learning Models Using Synthetic Datasets

  Publication

  • Z. Kowalczuk
  • J. Glinko

  - Year 2022

  In order to solve increasingly complex problems, the complexity of Deep Neural Networks also needs to be constantly increased, and therefore training such networks requires more and more data. Unfortunately, obtaining such massive real world training data to optimize neural networks parameters is a challenging and time-consuming task. To solve this problem, we propose an easy-touse and general approach to training deep learning...

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