MBaranPeker

MBaranPeker's Stars

• facebookresearch/segment-anything

  The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

  Language:Jupyter Notebook46.6k3056595.5k

• haotian-liu/LLaVA

  [NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.

  Language:Python19.1k1591.5k2.1k

• isl-org/Open3D

  Open3D: A Modern Library for 3D Data Processing

  Language:C++11.2k2023.8k2.3k

• onnx/models

  A collection of pre-trained, state-of-the-art models in the ONNX format

  Language:Jupyter Notebook7.7k1873901.4k

• ros2/ros2

  The Robot Operating System, is a meta operating system for robots.

  3.4k2341k656

• ros/ros

  Core ROS packages

  Language:Python2.8k208137771

• leggedrobotics/darknet_ros

  YOLO ROS: Real-Time Object Detection for ROS

  Language:C++2.2k493391.2k

• NVlabs/BundleSDF

  [CVPR 2023] BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects

  Language:Python99216164109

• ethnhe/PVN3D

  Code for "PVN3D: A Deep Point-wise 3D Keypoints Hough Voting Network for 6DoF Pose Estimation", CVPR 2020

  Language:Python48819113105

• stereolabs/zed-ros-wrapper

  ROS wrapper for the ZED SDK

  Language:C++44722804391

• cyberbotics/webots_ros2

  Webots ROS 2 packages

  Language:C39432274147

• yzrobot/adaptive_clustering

  [ROS package] Lightweight and Accurate Point Cloud Clustering

  Language:C++3679890

• alexfrom0815/Online-3D-BPP-DRL

  This repository contains the implementation of paper Online 3D Bin Packing with Constrained Deep Reinforcement Learning.

  Language:Python28872066

• ros-industrial/kuka_experimental

  Experimental packages for KUKA manipulators within ROS-Industrial (http://wiki.ros.org/kuka_experimental)

  Language:C++26534138213

• yehengchen/Computer-Vision-and-Robotics-Paper-List

  International Conferences and Journal on Computer Vision / Robotics

  998026

• IntelligentRoboticsLabs/gb_visual_detection_3d

  Language:C++8775532

• erensezener/kuka-rsi3-communicator

  For controlling KUKA manipulators via RSI 3

  Language:Python7211422

• jediofgever/ROS_Raw_Kitti_Player

  Ros Package to access and manipulate raw KITTI data, with Camera-LIDAR sensor fusion and Perception Tasks

  Language:C++7061112

• DavidFernandezChaves/Detectron2_ros

  A ROS Node for detecting objects using Detectron2.

  Language:Python514923

• ros-industrial/yak_ros

  Example ROS frontend node for the Yak TSDF package

  Language:C++4882022

• ignc-research/IR-DRL

  A training and testing platform for Industrial Robot applications.

  Language:Python32326

• cjlh/kuka-rsi-ros-interface

  A ROS node for the manipulation of a KUKA robot arm via RSI 3

  Language:C++29526

• MBaranPeker/Pursuit-Evasion-Game-with-Deep-Reinforcement-Learning-in-an-environment-with-an-obstacle

  In this study, a multi agent chase-escape problem using Deep Q learning. Actors of the problem are smart evader and smart pursuers with opposite goals. At the beginning of the game these agents have homogeneous properties and evader and pursuits don’t have knowledge about the map. The purpose of the pursuer robots is the catching the evader as fast as it could and the purpose of the evader robot is the escaping as much as it could. Such as this game, where a player's gain is in balance with the loss of other players are called zero-sum games. The end condition, which may differ according to the approach applied, in our study is that “any of the pursuers or evader within the same or neighbor pixel with obstacle or map border” or “one pursuer and evader within the same or neighbor pixel”, in other words, Evader catches by the any of the pursuers or evader hits an obstacle or any pursuers hits an obstacle. A new episode of the game resumes after each collision or cath. In this respect, escape-chase problems are also included in the repeat games class. In this study, the question is what any pursuer or evader can do to improve its performance in a repetitive part of the game is questioned. The method used for this study is Deep Reinforcement Learning. Agents receive rewards or penalties based on their moves within a section and update this information into the Neural Network.

  Language:Python17420

• jediofgever/guided_filter_point_cloud_denoising

  Denoising of Point Cloud with Guided Filter

  Language:C++147

• AlejoDiaz49/Object-SearchLocalization-Robot

  Language:C90310

• jediofgever/ROS_Self_Driving_Car_Sim

  Minimalistic Self Driving Car Simulation with basic Sensors and Perception Tasks

  Language:C++7504

• maggielovedd/3D-Point-Cloud-Curve-Extraction-and-Optimization

  This code is designed to extract a representative curve (or a thin center line) from a noisy 3D point cloud, especially when the point cloud has discernible geometric patterns. Given a noisy point cloud that has an inherent structure or shape, the goal is to identify and trace a continuous curve that best captures the essence of that shape.

  Language:Python6111

• MBaranPeker/Reinforcement-Learning-Tutorial

  Sample reinforcement learning tutorial notebooks 🎉

  Language:Jupyter Notebook10