XLerobot-X1

Ubuntu22.04

Joycon (Remote Control Joystick) 

Rhino Pi-X1 

Type‑C to DC 5.5mm PD Cable 

BambuStudio

3D Printed parts(Black)

3D printer

IKEA RÅSKOG Cart

MX5264 Terminal Wire 100cm 

Python  3.12

Camera 

Type-C to DC Cable (12V)

Portable Power Bank

Servo ST3215 - C018

Screwdriver set

Personal Computer

4-inch Omni Wheel 

MX5264-3P Reverse Terminal Wire 

Bus Servo Driver Board 

3D Printed parts(orange)

<html><body><h3></h3> <figure><iframe src="https://youtu.be/iqg3tBYALDs?si=T1tIG1RHrIBszAd5"></iframe></figure> <h3>Core Design Philosophy</h3>1.Modular and Scalable Design - Standardized Interfaces: Defined mechanical and electrical interfaces enable independent development and upgrade of the mobile platform and manipulation arms - Hardware-Software Decoupling: The control system employs a layered architecture, separating upper-level task planning from lower-level motion control - Progressive Capability Expansion: Allowing researchers to add sensors, actuators and other components incrementally based on specific needs 2.Cost Optimization and Performance Balance - Optimized Material Selection: 90% of the components are 3D-printed, combined with cost-effective motors and electronic parts - Workspace Optimization: The effective working range of the SO101 robotic arm (approximately 40 cm) is maximized through mechanical structural design - Optimized Control Strategy: A Bluetooth control solution is adopted to avoid the high costs of WiFi/5G communication 3.Safety and Reliability Design - Physical Safety Design: Physical constraints including low-torque motors, short arm length, and a wheeled base ensure the system poses minimal physical risk to humans - Control Safety Mechanisms: Software-imposed limits on motion range, speed and force prevent accidental operations - Fail-Safe Design: Redundant design is applied to key systems to ensure a single fault will not result in complete system failure <figure><img src="https://projects.arduinocontent.cc/8494325b-1717-40d1-878c-fd359cc9f3cc.png"/><figcaption></figcaption></figure> <h3>Core Technical Analysis</h3>1.Mechanical Structure and Kinematic Design - Equipped with dual SO-101 robotic arms, each with 6 degrees of freedom and a single-arm load capacity of 600–1000 g - Maximizes working range through optimized arm length and joint configuration, meeting basic operational needs in household scenarios - Applies topology optimization technology to 3D-printed parts to reduce weight while ensuring structural strength 2.Control System Architecture - Lower-Level Motion Control Layer: Real-time control via the Rhino Pi X1 (based on the QCS8550 platform) - Middle-Level Task Execution Layer: Implementing task decomposition and motion trajectory generation - Upper-Level Decision-Making and Learning Layer: Integrating AI models to support visual perception, task planning and learning 3.Sensor Fusion and Perception System - Multi-Modal Perception: Fusing data from vision, depth, touch and other sensors to enhance environmental understanding capabilities - Lightweight Processing: Implementing efficient perception algorithms on edge computing devices - Simulation-to-Real Transfer: Supporting rapid algorithm verification and migration via the Maniskill simulation environment <h3>Code Resources</h3>XLerobot-X1 code resources are based on the XLeRobot project, providing a unified platform for robotics development, including: * Robot control and teleoperation * Computer vision and perception * Machine learning and reinforcement learning * Simulation environments * Data collection and processing * Motor control and calibration Project Structure <pre>lerobot/ </pre><pre>├── examples/ # Example scripts and demonstrations </pre><pre>├── src/ # Source code </pre><pre>│ └── lerobot/ # Main package </pre><pre>│ ├── async_inference/ # Asynchronous inference for policy execution </pre><pre>│ ├── cameras/ # Camera modules and interfaces </pre><pre>│ ├── configs/ # Configuration management </pre><pre>│ ├── datasets/ # Dataset handling and processing </pre><pre>│ ├── envs/ # Simulation environments </pre><pre>│ ├── motors/ # Motor control and calibration </pre><pre>│ ├── optim/ # Optimization tools for training </pre><pre>│ ├── policies/ # Policy implementations </pre><pre>│ ├── rl/ # Reinforcement learning utilities </pre><pre>│ ├── robots/ # Robot implementations </pre><pre>│ ├── scripts/ # Command-line scripts </pre><pre>│ ├── teleoperators/ # Teleoperation interfaces </pre><pre>│ ├── transport/ # Communication utilities </pre><pre>│ ├── utils/ # General utility functions </pre><pre>│ ├── __init__.py # Module initialization </pre><pre>│ └── __version__.py # Version information </pre><pre>├── .gitignore # Git ignore file </pre><pre>├── LICENSE # License file </pre><pre>├── README.md # README file </pre><pre>├── README_zh.md # Chinese README file </pre><pre>├── pyproject.toml # Project configuration </pre><pre>└── requirements.txt # Dependencies </pre><h3></h3><h3>Getting Started</h3>Install Joy-Con Driver <pre># Clone the repository </pre><pre>git clone https://github.com/box2ai-robotics/joycon-robotics.git </pre><pre>cd joycon-robotics </pre><pre>pip install -e . </pre><pre>sudo apt-get update </pre><pre>	 </pre><pre>sudo apt-get install -y libevdev-dev libudev-dev cmake </pre><pre>	 </pre><pre># Before running make install, perform the following steps: </pre><pre># 1. Open Makefile and remove 'install_nintendo' from the second-to-last line, then save </pre><pre># 2. Create temporary directory </pre><pre>mkdir /tmp/joycon-install-temp </pre><pre># 3. Execute installation </pre><pre>make install </pre><pre>	 </pre><pre># Install dependencies </pre><pre>pip install lerobot </pre><pre>	 </pre><pre># Install servo motor control library </pre><pre>pip install feetech-servo-sdk==1.0.0 </pre>Basic Usage Example Code Startup Example code is provided in the Code_Resources/XLeRobot-X1/examples/ directory. You can start them as follows: <pre># Joy-Con controller test </pre><pre>python 1_test_joycon.py </pre><pre># XLeRobot 3Wheels teleoperation </pre><pre>python 7_xlerobot_3wheels_teleop_joycon.py </pre><pre># XLeRobot 3Wheels calibration tool </pre><pre>python 7b_xlerobot_3wheels_calibration.py </pre> <h3>Application Scenarios</h3><figure><img src="https://projects.arduinocontent.cc/6411c0cb-008f-4024-a621-69c7ebfcc70d.png"/><figcaption></figcaption></figure> 1.Academic Research Scenarios - Fundamental Research on Embodied AI: Investigating core issues such as physical interaction and environmental adaptation - Algorithm Verification: Providing a low-cost environment for verifying new control and learning algorithms - Interdisciplinary Research: Promoting cross-disciplinary research in robotics, AI, cognitive science and other fields 2.Educational Application Scenarios - STEM Education: Providing a practical platform for cutting-edge robotics technology for high school educators - Higher Education: Serving as a cost-effective platform for university robotics and AI courses - Student Research: Supporting a complete learning path from beginners to advanced researchers 3.Household Application Scenarios - Daily Housework Assistance: Performing simple household chores such as item delivery and basic organization - Home Automation: Acting as a physical interaction interface for smart home systems - Security Monitoring: Realizing basic home security monitoring in combination with visual systems 4.Commercial Application Scenarios - Product Prototype Development: Enabling faster prototype development with the world's most affordable modular robot platform - Technology Verification: Verifying the feasibility of new technologies in a low-cost environment - Market Exploration: Exploring potential demand and application scenarios in the household robot market </body></html>

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