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An open source quadruped robot pet framework for developing Boston Dynamics-style four-legged robots that are perfect for STEM, coding & robotics education, IoT robotics applications, AI-enhanced robotics application services, research, and DIY robotics kit development.
A computationally efficient and robust LiDAR-inertial odometry (LIO) package
Cross-platform ground control station for drones (Android, iOS, Mac OS, Linux, Windows)
An open and affordable mini quadruped robot based on ESP32.
ROS 2 Navigation Framework and System
[CVPR 2023 Best Paper Award] Planning-oriented Autonomous Driving
An optimization-based multi-sensor state estimator
Webots Robot Simulator
[RSS 2023] Diffusion Policy Visuomotor Policy Learning via Action Diffusion
Open Source Robotic Arm for All Developers
Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
NVIDIA Isaac Sim™ is an open-source application on NVIDIA Omniverse for developing, simulating, and testing AI-driven robots in realistic virtual environments.
Python package for the evaluation of odometry and SLAM
A collection of high-quality models for the MuJoCo physics engine, curated by Google DeepMind.
INAV: Navigation-enabled flight control software
Behavior Trees Library in C++. Batteries included.
Model-based design and verification for robotics.
JavaScript framework for robotics, drones, and the Internet of Things (IoT)
RTAB-Map library and standalone application
DORA (Dataflow-Oriented Robotic Architecture) is middleware designed to streamline and simplify the creation of AI-based robotic applications. It offers low latency, composable, and distributed dataflow capabilities. Applications are modeled as directed graphs, also referred to as pipelines.
StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
全球最小的桌面级双轮腿机器人！
GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.