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Detecting robot grasping positions with deep neural networks. The model is trained on Cornell Grasping Dataset. This is an implementation mainly based on the paper 'Real-Time Grasp Detection Using Convolutional Neural Networks' from Redmon and Angelova.
This repository was created for the subject of Computer Theory. The propose of this subject is to improve your skills to solve the 0-1 knapsack problem of different ways. The techniques used were Dynamic Programing and two metaheuristics (which are GRASP and TABU search).
A computer vision based project which requires to figure out hands in hundreds of First-person Video Camera photos and then classify those hands with different hand grasp gestures.
[ICLR 2022] The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training by Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy
This project focuses on training robots to grasp everyday objects accurately. We gather a unique point cloud dataset using an iPhone's LiDAR and process it with Polycam. We develop a PointNet model from scratch to perform multi-class classification and part-segmentation, guiding the robot on where to grasp objects.