In robotics development, establishing intuitive and natural human-robot interaction is a significant challenge. By combining mature computer vision technology with open-source robotic platforms, we can explore practical and valuable solutions. This article takes the TonyPi Humanoid Robot by Hiwonder as an example to demonstrate a complete workflow for real-time robot control using human body movements, leveraging the MediaPipe pose detection model and Python. The project code is fully open-source, providing an excellent hands-on platform for learning about robotic motion control and visual interaction.
🔥Access TonyPi Resources: codes, tutorials, experimental cases and schematics.Project Overview: A Real-Time Loop from Visual Capture to Physical Imitation
The core objective of this project is to build a low-latency, real-time system that accomplishes the following workflow:
- Visual Perception: Capture a video stream of the user in front of the robot via its onboard camera.
- Pose Detection: Use the MediaPipe model to detect human poses in real-time and extract 2D coordinates for 33 key body landmarks.
- Action Parsing: Calculate joint angles and limb poses based on the coordinates of key points (e.g., shoulders, elbows, wrists).
- Motion Mapping & Execution: Map the calculated joint angles to control commands for the corresponding servos on the TonyPi robot through kinematic transformations, driving it to perform imitative actions.
The final result is this: when you raise your arm, bend your elbow, or turn your body in front of the robot, TonyPi can mimic your upper body movements almost synchronously, achieving an intuitive "mirroring" control effect.
💡View the full TonyPi project on GitHub. Follow Hiwonder GitHub for the latest updates.Analysis of the Core Technology Stack
Achieving this interactive effect relies on the coordinated operation of several technical layers:
1. MediaPipe Pose Detection
MediaPipe provides a lightweight yet efficient machine learning model capable of tracking 33 3D key points of the human body in real-time on common computing devices (like the Raspberry Pi). In our application, we primarily focus on upper-body key points such as shoulders, elbows, wrists, and hips, which form the core skeleton for controlling the robot's arms and torso.
2. Keypoint-Based Action Parsing Algorithm
After obtaining keypoint coordinates, they must be translated into meaningful action commands. For example:
- Detecting a Raised Arm: Achieved by calculating the positional difference of the wrist keypoint relative to the shoulder keypoint along the Y-axis.
- Calculating Elbow Bend Angle: Using the coordinates of the shoulder, elbow, and wrist points to compute the included angle via the vector dot product formula.
These calculations transform continuous visual data into discrete, quantifiable descriptions of joint states.
3. Robotic Kinematics and Servo Control
This is the crucial step for converting "digital posture" into "physical motion." It requires establishing a mapping relationship between human joints and the TonyPi robot's servos.
For instance:
- Mapping the calculated human elbow angle proportionally to the robot's elbow servo control range of 0-180 degrees.
- Safety threshold limits must be incorporated to account for the robot's physical movement constraints, preventing servo damage due to overflow in angle calculations.
Finally, the generated angle commands are sent to the robot's main controller via Python's serial or bus communication libraries.
4. Low-Latency System Optimization
To achieve "real-time" imitation, the entire processing pipeline needs optimization:
- Reduce camera image resolution to speed up processing.
- Optimize the calculation logic for keypoint coordinates at the code level.
- Ensure the servo control command transmission frequency is high enough for smooth, non-jerky movements.
All Python code for the TonyPi project is open-source, making it a powerful learning tool beyond just a demonstration. Through it, you can gain in-depth knowledge of:
Computer Vision Practice: Gain hands-on experience integrating and using mainstream AI models like MediaPipe, understanding the practical application of keypoint detection.
Fundamentals of Robotic Kinematics: Intuitively understand the relationship between joint space and actuator control by writing mapping code.
Real-Time System Concepts: Experience the complete feedback loop of processing sensor data, performing intermediate calculations, and outputting control signals—a core concept in robotics and automation.
Creative Project Expansion:
- Modify the code to recognize specific gestures (like waving) to trigger predefined action sequences.
- Develop a "dance learning mode" that records a series of poses for the robot to loop and reproduce.
- Attempt to integrate voice control for multimodal interaction, such as responding to the command "raise your left hand."
By integrating MediaPipe with an open-source robotic platform, we have successfully built a real-time human pose imitation system. This project clearly demonstrates the complete technical pathway from visual perception to physical action. It deconstructs seemingly complex human-robot interaction into modular engineering practices that are learnable, modifiable, and extensible. For developers, students, and enthusiasts looking to enter the fields of robotics, computer vision, or human-computer interaction, studying and improving such an open-source project is an excellent way to accumulate practical experience and understand the concepts of system integration.
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