carla - Open-source simulator for autonomous driving research.

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CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. If you want to benchmark your model in the same conditions as in our CoRL’17 paper, check out Benchmarking.

simulator autonomous-vehicles autonomous-driving research ai artificial-intelligence computer-vision deep-learning deep-reinforcement-learning imitation-learning self-driving-car ue4 unreal-engine-4 cross-platform

simulator - A ROS/ROS2 Multi-robot Simulator for Autonomous Vehicles

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Check out our latest news and subscribe to our mailing list to get the latest updates. LG Electronics America R&D Lab has developed an HDRP Unity-based multi-robot simulator for autonomous vehicle developers. We provide an out-of-the-box solution which can meet the needs of developers wishing to focus on testing their autonomous vehicle algorithms. It currently has integration with The Autoware Foundation's Autoware.auto and Baidu's Apollo platforms, can generate HD maps, and can be immediately used for testing and validation of a whole system with little need for custom integrations. We hope to build a collaborative community among robotics and autonomous vehicle developers by open sourcing our efforts.

api machine-learning simulator reinforcement-learning computer-vision deep-learning game-engine unity tensorflow artificial-intelligence ros autonomous self-driving-car unreal-engine baidu 3d airsim autoware carla

redtail - Perception and AI components for autonomous mobile robotics.

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Autonomous visual navigation components for drones and ground vehicles using deep learning. Refer to wiki for more information on how to get started. This project contains deep neural networks, computer vision and control code, hardware instructions and other artifacts that allow users to build a drone or a ground vehicle which can autonomously navigate through highly unstructured environments like forest trails, sidewalks, etc. Our TrailNet DNN for visual navigation is running on NVIDIA's Jetson embedded platform. Our arXiv paper describes TrailNet and other runtime modules in detail.

ai computer-vision deep-learning robotics artificial-intelligence drones jetson

carma-platform - CARMA Platform is built on robot operating system (ROS) and utilizes open source software (OSS) that enables Cooperative Driving Automation (CDA) features to allow Automated Driving Systems to interact and cooperate with infrastructure and other vehicles through communication

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CARMASM advances research and development to accelerate market readiness and deployment of cooperative driving automation, while advancing automated driving technology safety, security, data, and artificial intelligence. It encourages collaboration and participation by a community of engineers and researchers to advance understanding of cooperative driving automation using open source software (OSS) and agile project management practices. CARMA is a reusable, extensible platform for controlling SAE level 2 connected, automated vehicles (AVs). It provides a rich, generic API for third party plugins that implement vehicle guidance algorithms to plan vehicle trajectories. It is written in C++ and runs in a Robot Operating System (ROS) environment on Ubuntu. The platform can be reused on a variety of properly equipped vehicles. Migration has begun from the ROS 1 framework to ROS 2.

open-source cpp ros autonomous-driving autonomous-vehicles self-driving cooperative-driving-automation automated-vehicles

deepdrive - Deepdrive is a simulator that allows anyone with a PC to push the state-of-the-art in self-driving

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Note that we recorded the baseline dataset in sync mode which is much slower than async mode. Async mode probably is fine to record in, we just haven't got around to trying it out for v3.

competition control reinforcement-learning deep-learning simulation tensorflow deep-reinforcement-learning vision gym self-driving-car unreal-engine transfer-learning sensorimotor

webots - Webots Robot Simulator

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Webots is an open-source robot simulator released under the terms of the Apache 2.0 license. It provides a complete development environment to model, program and simulate robots, vehicles and biomechanical systems. You can download pre-compiled binaries for Windows, macOS and Linux of the latest release, as well as older releases and nightly builds.

open-source multi-platform simulator robot ai computer-vision robotics simulation physics-engine ros robots autonomous-vehicles fluid-dynamics 3d-engine robot-simulator webots robotics-simulation simulated-robots

apollo - An open autonomous driving platform

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Apollo is a high performance, flexible architecture which accelerates the development, testing, and deployment of Autonomous Vehicles. For business and partnership, please visit our website.

apollo autonomous-vehicles autonomous-driving autonomy self-driving-car

BotSharp - The Open Source AI Chatbot Platform Builder in 100% C# Running in

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BotSharp is an open source machine learning framework for AI Bot platform builder. This project involves natural language understanding, computer vision and audio processing technologies, and aims to promote the development and application of intelligent robot assistants in information systems. Out-of-the-box machine learning algorithms allow ordinary programmers to develop artificial intelligence applications faster and easier. It's witten in C# running on .Net Core that is full cross-platform framework. C# is a enterprise grade programming language which is widely used to code business logic in information management related system. More friendly to corporate developers. BotSharp adopts machine learning algrithm in C# directly. That will facilitate the feature of the typed language C#, and be more easier when refactoring code in system scope.

artificial-intelligence natural-language-processing chatbot chatbot-framework nlp nlu automation

ImageAI - A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

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A python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.

artificial-intelligence machine-learning prediction image-prediction python3 offline-capable imageai artificial-neural-networks algorithm image-recognition object-detection squeezenet densenet video inceptionv3 detection gpu ai-practice-recommendations

dataset-api - The ApolloScape Open Dataset for Autonomous Driving and its Application.

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Apollo is a high performance, flexible architecture which accelerates the development, testing, and deployment of Autonomous Vehicles. ApolloScape, part of the Apollo project for autonomous driving, is a research-oriented dataset and toolkit to foster innovations in all aspects of autonomous driving, from perception, navigation, control, to simulation.

dataset autonomous-driving trajectory-prediction video-inpainting 3d-lidar apolloscape-dataset 3d-car-instance

habitat-lab - A modular high-level library to train embodied AI agents across a variety of tasks, environments, and simulators

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Habitat Lab is a modular high-level library for end-to-end development in embodied AI -- defining embodied AI tasks (e.g. navigation, instruction following, question answering), configuring embodied agents (physical form, sensors, capabilities), training these agents (via imitation or reinforcement learning, or no learning at all as in classical SLAM), and benchmarking their performance on the defined tasks using standard metrics. Habitat Lab currently uses Habitat-Sim as the core simulator, but is designed with a modular abstraction for the simulator backend to maintain compatibility over multiple simulators. For documentation refer here.

simulator research reinforcement-learning ai computer-vision deep-learning robotics deep-reinforcement-learning sim2real

awesome-robotic-tooling - Tooling for professional robotic development in C++ and Python with a touch of ROS, autonomous driving and aerospace: https://freerobotics

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To stop reinventing the wheel you need to know about the wheel. This list is an attempt to show the variety of open and free tools in software and hardware development, which are useful in professional robotic development. Your contribution is necessary to keep this list alive, increase the quality and to expand it. You can read more about it's origin and how you can participate in the contribution guide and related blog post. All new project entries will have a tweet from protontypes.

machine-learning awesome robot cplusplus cpp robotics mapping aerospace point-cloud artificial-intelligence ros lidar self-driving-car awesome-list automotive slam autonomous-driving robotic ros2

GibsonEnv - Gibson Environments: Real-World Perception for Embodied Agents

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You shouldn't play video games all day, so shouldn't your AI! We built a virtual environment simulator, Gibson, that offers real-world experience for learning perception. I. being from the real-world and reflecting its semantic complexity through virtualizing real spaces, II. having a baked-in mechanism for transferring to real-world (Goggles function), and III. embodiment of the agent and making it subject to constraints of space and physics via integrating a physics engine (Bulletphysics).

computer-vision robotics simulator sim2real deep-learning deep-reinforcement-learning research ros reinforcement-learning cvpr2018

tensorwatch - Debugging, monitoring and visualization for Deep Learning and Reinforcement Learning

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TensorWatch is a debugging and visualization tool designed for deep learning and reinforcement learning. It fully leverages Jupyter Notebook to show real time visualizations and offers unique capabilities to query the live training process without having to sprinkle logging statements all over. You can also use TensorWatch to build your own UIs and dashboards. In addition, TensorWatch leverages several excellent libraries for visualizing model graph, review model statistics, explain prediction and so on. TensorWatch is under heavy development with a goal of providing a research platform for debugging machine learning in one easy to use, extensible and hackable package.

ai deep-learning deeplearning machine-learning machinelearning machinelearning-python reinforcement-learning debugging debugging-tool debugger-visualizer debug monitoring explainable-ai explainable-ml saliency salient-object-detection model-visualization

ai-resources - Selection of resources to learn Artificial Intelligence / Machine Learning / Statistical Inference / Deep Learning / Reinforcement Learning

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Update April 2017: It’s been almost a year since I posted this list of resources, and over the year there’s been an explosion of articles, videos, books, tutorials etc on the subject — even an explosion of ‘lists of resources’ such as this one. It’s impossible for me to keep this up to date. However, the one resource I would like to add is https://ml4a.github.io/ (https://github.com/ml4a) led by Gene Kogan. It’s specifically aimed at artists and the creative coding community. This is a very incomplete and subjective selection of resources to learn about the algorithms and maths of Artificial Intelligence (AI) / Machine Learning (ML) / Statistical Inference (SI) / Deep Learning (DL) / Reinforcement Learning (RL). It is aimed at beginners (those without Computer Science background and not knowing anything about these subjects) and hopes to take them to quite advanced levels (able to read and understand DL papers). It is not an exhaustive list and only contains some of the learning materials that I have personally completed so that I can include brief personal comments on them. It is also by no means the best path to follow (nowadays most MOOCs have full paths all the way from basic statistics and linear algebra to ML/DL). But this is the path I took and in a sense it's a partial documentation of my personal journey into DL (actually I bounced around all of these back and forth like crazy). As someone who has no formal background in Computer Science (but has been programming for many years), the language, notation and concepts of ML/SI/DL and even CS was completely alien to me, and the learning curve was not only steep, but vertical, treacherous and slippery like ice.

UETorch - A Torch plugin for Unreal Engine 4.

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UETorch is an Unreal Engine 4 plugin that adds support for embedded Lua/Torch scripts in the game engine loop, and a set of Lua APIs for providing user input, taking screenshots and segmentation masks, controlling game state, running faster than real time, etc. Torch is an AI Research platform that is focused on deep learning. UETorch strongly leverages the sparsely documented ScriptPlugin plugin provided with Unreal Engine 4.Some recent research done using the UETorch platform is detailed in this paper "Learning Physical Intuition of Block Towers by Example" where we explore the ability of deep feed-forward models to learn intuitive physics.

UETorch - A Torch plugin for Unreal Engine 4.

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UETorch is an Unreal Engine 4 plugin that adds support for embedded Lua/Torch scripts in the game engine loop, and a set of Lua APIs for providing user input, taking screenshots and segmentation masks, controlling game state, running faster than real time, etc. Torch is an AI Research platform that is focused on deep learning. UETorch strongly leverages the sparsely documented ScriptPlugin plugin provided with Unreal Engine 4. Some recent research done using the UETorch platform is detailed in this paper "Learning Physical Intuition of Block Towers by Example" where we explore the ability of deep feed-forward models to learn intuitive physics.

self-driving-car - The Udacity open source self-driving car project

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At Udacity, we believe in democratizing education. How can we provide opportunity to everyone on the planet? We also believe in teaching really amazing and useful subject matter. When we decided to build the Self-Driving Car Nanodegree program, to teach the world to build autonomous vehicles, we instantly knew we had to tackle our own self-driving car too. You can read more about our plans for this project.

ELF - An End-To-End, Lightweight and Flexible Platform for Game Research

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ELF is an Extensive, Lightweight and Flexible platform for game research, in particular for real-time strategy (RTS) games. On the C++-side, ELF hosts multiple games in parallel with C++ threading. On the Python side, ELF returns one batch of game state at a time, making it very friendly for modern RL. In comparison, other platforms (e.g., OpenAI Gym) wraps one single game instance with one Python interface. This makes concurrent game execution a bit complicated, which is a requirement of many modern reinforcement learning algorithms. Besides, ELF now also provides a Python version for running concurrent game environments, by Python multiprocessing with ZeroMQ inter-process communication. See ./ex_elfpy.py for a simple example.

gaming cpp artificial-intelligence deep-learning neural-network platform reinforcement-learning

highway-env - A minimalist environment for decision-making in autonomous driving

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An episode of one of the environments available in highway-env. In this task, the ego-vehicle is driving on a multilane highway populated with other vehicles. The agent's objective is to reach a high speed while avoiding collisions with neighbouring vehicles. Driving on the right side of the road is also rewarded.

reinforcement-learning autonomous-driving gym-environment