[ICRA 2022] CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation

GRAB: A Dataset of Whole-Body Human Grasping of Objects

Toolbox for our GraspNet-1Billion dataset.

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.

An implementation of our RA-L work 'Real-world Multi-object, Multi-grasp Detection'

[ICRA 2024]ASGrasp: Generalizable Transparent Object Reconstruction and 6-DoF Grasp Detection from RGB-D Active Stereo Camera

[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

[ECCV 2022] TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance

This is my implementation of a branch and price algorithm to solve the humanitarian aid distribution problem. This problem is a VRP with a specific objective function

Vision-based robotic arm grasping using deep reinforcement learning

Scripts to generate and process point clouds in YCB dataset.

Source code for the "GRASP: Guiding model with RelAtional Semantics using Prompt"

[ICRA'25] NeuGrasp: Generalizable Neural Surface Reconstruction with Background Priors for Material-Agnostic Object Grasp Detection

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.

Python bindings for OptFrame C++ Functional Core

Solving SOP with SA, GRASP, Tabu Search algorithms. Include an analytic report.

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).

Heuristics, Metaheuristics and Instance Generator

ROS wrapper for M2T2 grasp predictor

Somos uma loja de venda de desktops e notebooks. Nossos produtos são os melhores do mercado!