This repository is a complete ROS2 software development kit for the Unitree Go2 quadruped robot. It provides a high-level SDK (go2_robot_sdk) with clean architecture (domain, application, infrastructure, presentation) that enables connection to the robot via WebRTC (Wi-Fi) or CycloneDDS (Ethernet), reception and decoding of real-time data (LiDAR, IMU, joint states, camera, odometry), and control commands (movement, stance, joystick). It includes multiple ROS2 packages: go2_interfaces (25+ custom message types), lidar_processor (Python and C++ nodes for LiDAR point cloud decoding and aggregation), speech_processor (ElevenLabs TTS with caching and robot playback), coco_detector (object detection using PyTorch FasterRCNN), and launch files for SLAM (slam_toolbox) and autonomous navigation (Nav2). The repository also contains URDF models, calibration files, RViz configurations, Docker support, and multi-robot namespace remapping. While primarily an application for controlling and monitoring the Go2 robot, it includes data-processing components (LiDAR decoding, IMU filtering, object detection) that give it a hybrid data-research character. The codebase demonstrates professional ROS2 development practices with modular design, abstract interfaces, and multiple language implementations.
This repository is a curated collection of robot description files (URDF, xacro, STL/DAE meshes) for multiple mobile manipulator platforms (Unitree B1+Z1, Aliengo+Z1, Go2+ARX, PR2, Ridgeback+UR5, Mabi Mobile) combined with an extensive awesome-list survey of loco-manipulation research papers. The robot models are well-structured with parameterized xacro macros, detailed inertial parameters, and simulation-ready Gazebo plugins. The paper list is meticulously organized by methodology (model-based control, learning-based, foundation models) with verified links. While the repository contains no application code or data pipelines, it serves as a valuable reference for researchers and engineers needing ready-to-use robot models for simulation or a comprehensive literature overview. The primary value lies in the reusable xacro patterns and the curated paper index.
This repository implements a hierarchical controller for quadruped locomotion that combines a high-level reinforcement learning policy with a low-level model predictive controller (MPC). The RL policy dynamically predicts weight parameters for the MPC, which computes optimal ground reaction forces and joint torques. The codebase includes Isaac Gym training environments for the Unitree A1, Go1, and Aliengo robots, along with a custom C++ MPC solver (OSQP-based) and a finite state machine for robust transient handling. While primarily a research project that bridges RL and MPC for legged robots, it also provides reusable robotics components such as state estimators, leg controllers, gait pattern generators, and foot swing trajectory planners. The work is based on the MIT Cheetah software and includes detailed development logs and configuration files for reproducible experiments.
This repository provides the firmware and desktop tools for Petoi's quadruped robots (Bittle, Nybble, Chero). The core is an Arduino-based firmware (OpenCat.ino) running on ATmega328P (NyBoard) that handles servo control, gait generation, IMU integration, sensor processing (IR, ultrasonic, gesture, voice), and skill execution via pre-programmed motion sequences. A Python desktop application (pyUI) offers firmware upload, joint calibration, skill composition, and debugging. A Python serial API (serialMaster, OpenCatPythonAPI) enables scripting robot behaviors. The architecture is monolithic firmware with conditional compilation for multiple hardware variants and sensor options. While the project has a large user base and extensive documentation, the codebase shows signs of technical drift: it is explicitly legacy (development moved to OpenCatESP32), relies on outdated libraries, has duplicated code (e.g., three copies of IRremote), and uses global state and hardcoded paths. The skill system and serial protocol are well-defined patterns, but overall reusability is limited by tight coupling to specific hardware and board versions.