How do I set up CHAMP for my custom quadruped robot?

Use the setup-assistant GUI to generate a configuration package, then follow the hardware integration guidelines for Linux machines or the lightweight firmware for microcontrollers. Build the workspace with catkin_make to get started.

Can CHAMP work with ROS2 or is it only for ROS1?

CHAMP is designed for ROS1 (Kinetic and Melodic) and not officially supported on ROS2. Porting would require significant adaptation, as the framework relies on ROS1-specific packages and workflows.

What's the difference between CHAMP and MIT Cheetah software?

CHAMP is an open-source framework based on MIT Cheetah control algorithms but provides a complete ROS ecosystem with simulation and configuration tools. MIT's software is often more research-focused and may lack pre-built integration for hobbyist or DIY projects.

How to tune gait parameters for stable walking in CHAMP?

Edit the gait.yaml file in your configuration package, adjusting parameters like stance duration and leg swing height. The setup-assistant offers a GUI for initial tuning, but fine-tuning may require experimentation based on robot dynamics.

Is CHAMP suitable for real-time control on microcontrollers like Teensy?

Yes, the lightweight library can run on Teensy for direct actuator control, but real-time performance depends on the microcontroller's capabilities and code optimizations; it's best for moderate-frequency control loops.

Can I simulate multiple robots in Gazebo with CHAMP?

Yes, use the spawn_world.launch with unique robot names to avoid topic clashes, as detailed in the README. This allows testing multi-robot scenarios but requires careful configuration of transforms and parameters.

Open-Awesome

champ

BSD-3-ClauseC++

An open-source ROS-based quadruped controller framework for building and controlling legged robots, based on MIT Cheetah research.

GitHub

2.2k stars434 forks0 contributors

What is champ?

CHAMP is an open-source ROS-based quadruped controller framework that implements the MIT Cheetah control algorithms for legged robots. It provides tools for configuring, simulating, and autonomously operating custom quadruped robots, enabling researchers and developers to build and test advanced locomotion systems without proprietary software.

Target Audience

Robotics researchers, hobbyists building DIY quadrupeds (e.g., SpotMicroAI), and developers working on legged robot control and autonomous navigation.

Value Proposition

CHAMP offers a complete, modular framework with pre-configured robot models, simulation support, and a lightweight library that runs on both SBCs and microcontrollers, making advanced quadruped control accessible to open-source projects.

Overview

MIT Cheetah I Implementation

Use Cases

Best For

Building custom quadruped robots from scratch
Simulating legged robot locomotion in Gazebo
Implementing autonomous navigation for quadrupeds using ROS
Configuring gait parameters for stable walking and running
Running quadruped control algorithms on microcontrollers like Teensy
Integrating with DIY open-source quadruped projects like SpotMicroAI

Not Ideal For

Projects targeting ROS2 or newer robotics frameworks without legacy ROS1 support
Teams developing non-quadruped legged robots like bipeds or hexapods
Applications requiring hard real-time control without ROS modifications or custom middleware
Simple hobby projects where a basic, pre-made controller without ROS dependency suffices

Pros & Cons

Pros

Pre-built Robot Models

Includes URDFs for popular platforms like MIT Mini Cheetah and Boston Dynamics Spot, saving significant modeling and configuration time.

GUI Setup Assistant

Provides a graphical tool for configuring custom robots, including URDF generation and gait parameter tuning, making it accessible to non-experts.

Lightweight Microcontroller Support

Header-only C++ library runs on microcontrollers like Teensy, enabling direct actuator control for resource-constrained deployments.

Integrated ROS Autonomy

Seamlessly integrates with ROS navigation stack for out-of-the-box SLAM and goal-based movement, reducing development overhead.

Cons

Outdated ROS Support

Officially tested only on Ubuntu 16.04/18.04 with ROS Kinetic/Melodic, requiring extra effort for newer distributions and lacking ROS2 compatibility.

Complex Initial Setup

Installation involves cloning multiple repositories, managing dependencies with rosdep, and catkin builds, which can be error-prone for newcomers.

Gazebo Tuning Overhead

Configuring physics parameters like mass and inertia in Gazebo requires manual tuning and external resources, as admitted in the README with links to guides.

Frequently Asked Questions

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