Popular GitHub Repositories for Grasp

Detect 6-DOF grasp poses in point clouds

Achieve the llama3 inference step-by-step, grasp the core concepts, master the process derivation, implement the code.

A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances.

Pytorch implementation of diffusion models on Lie Groups for 6D grasp pose generation https://sites.google.com/view/se3dif/home

GrabNet: A Generative model to generate realistic 3D hands grasping unseen objects (ECCV2020)

Dataset and Code for ICRA 2024 paper "Grasp-Anything: Large-scale Grasp Dataset from Foundation Models."

Deep learning for grasp detection within MoveIt.

code implementation of GraspGPT and FoundationGrasp

Isaaclab-based grasp learning test bench

ContactGen: Generative Contact Modeling for Grasp Generation (ICCV 2023)

End to end machine leanring project: This repository serves as a simplified guide to help you grasp the fundamentals of MLOps.

OpenDeepArxiv is an open-sourced project designed to streamline the process of searching for research papers on arXiv, filtering based on relevance, and generating comprehensive PDF reports complete with summaries. This pipeline leverages state-of-the-art summarization techniques to help researchers quickly grasp the latest findings in their fields

ROS2 Humble: Use voice commands to make the robot grasp the target object. ROS2 humble版本仿真环境下，实现接收语音命令后通过大模型转为指令，控制轮式机器人完成检测目标、建图导航并抓取物体。

Vision-based robotic arm grasping using deep reinforcement learning

The repository is for developing manipulator which can recognize the objects via image processing. The Jinyoung wants to grasp some objects with Opencv, ROS, MoveIt, and so on.

? Grasp - Pick-and-place with a robotic hand ??‍?

The text summarizer project is an innovative tool designed to condense lengthy textual content into concise and informative summaries, enabling users to efficiently grasp the main ideas and key points of the original text.

All you need to grasp computer vision applications without being overwhelmed!

Deep learning techniques for detecting 6 types of hand movements from labeled dataset procured from Kaggle, Grasp-and-Lift EEG Detection.

Adapted GRASP algorithm developed by researchers from the Informatics Center of the Federal University of Pernambuco - UFPE to optimize Deep Learning architectures for tabular data classification.