John Reidar Mathiassen
John Reidar Mathiassen
Publikasjoner og ansvarsområder
Teaching a robot to grasp real fish by imitation learning from a human supervisor in virtual reality
We teach a real robot to grasp real fish, by training a virtual robot exclusively in virtual reality. Our approach implements robot imitation learning from a human supervisor in virtual reality. A deep 3D convolutional neural network computes grasps from a 3D occupancy grid obtained from depth...
Robotic Handling of Compliant Food Objects by Robust Learning from Demonstration
The robotic handling of compliant and deformable food raw materials, characterized by high biological variation, complex geometrical 3D shapes, and mechanical structures and texture, is currently in huge demand in the ocean space, agricultural, and food industries. Many tasks in these industries are...
Bin Picking of Reflective Steel Parts Using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environment
We consider the case of robotic bin picking of reflective steel parts, using a structured light 3D camera as a depth imaging device. In this paper, we present a new method for bin picking, based on a dual-resolution convolutional neural network trained entirely in a simulated environment. The dual...
Bin Picking of Reflective Steel Parts using a Dual-Resolution Convolutional Neural Network Trained in a Simulated Environment
Grasping virtual fish: A step towards deep learning from demonstration in virtual reality
We present an approach to robotic deep learning from demonstration in virtual reality, which combines a deep 3D convolutional neural network, for grasp detection from 3D point clouds, with domain randomization to generate a large training data set. The use of virtual reality (VR) enables robot...
Learning robots in the seafood industry
Simplifying automation in the food industry using deep learning and virtual reality
Grasping Virtual Fish - A Step Towards Robotic Deep Learning from Demonstration in Virtual Reality
Robust classification approach for segmentation of blood defects in cod fillets based on deep convolutional neural networks and support vector machines and calculation of gripper vectors for robotic processing
Despite advances in computer vision and segmentation techniques, the segmentation of food defects such as blood spots, exhibiting a high degree of randomness and biological variation in size and coloration degree, has proven to be extremely challenging and it is not successfully resolved. Therefore...