How to Control TonyPi with Your Body Movements?

Let's explore the "Pose Control" feature to see exactly how you can control TonyPi with your body for a "move as you move" real-time experience.

Step 1: Start-Up and Detection. Stand in front of TonyPi's camera. The system uses the MediaPipe pose detection model to capture your upper body posture in real time. Key skeletal points like shoulders, elbows, and wrists are immediately highlighted on the screen, forming a clear human skeleton wireframe.

Step 2: Pose Analysis and Mapping. When you spread your arms, the system calculates the angle and positional changes of your arm keypoints in real time and instantly drives TonyPi's arm servos to perform the exact same spreading motion.

Step 3: Action Imitation and Extension. Raise your left arm, and the robot raises its left arm in sync. When you move your arms and legs into different poses, the robot accurately mimics these asymmetrical whole-body movements, achieving complex action following. From basic pose control to dynamic dance imitation, TonyPi keeps in perfect step with you, offering flexible and fun interaction.

This pipeline translates human posture into robotic joint motion in real time, achieving a seamless transition from "visual capture" to "physical mimicry" and delivering a futuristic human-robot interaction experience.

Achieving such fluid and precise action imitation relies on TonyPi's multi-layered technical architecture:

● MediaPipe Full-Body Pose Detection Model: Processes the camera feed in real time, stably tracking 33 body keypoints. It focuses particularly on the precise localization of upper-body joints like shoulders, elbows, and wrists, building a complete digital model of human posture.

● Coordinate-Based Motion Analysis Algorithm: The system uses changes in the 2D coordinates of keypoints to calculate joint angles and limb orientation in real time. For example, by comparing the relative positions of the shoulder, elbow, and wrist, it accurately determines whether an arm is spread, bent, or raised, enabling precise recognition of movement intent.

● Kinematic Mapping and Servo Control: Using kinematic algorithms, the system converts the detected human joint angles into control commands for TonyPi's corresponding servos, ensuring precise reproduction of the posture for natural and coordinated motion translation.

● Low-Latency Real-Time Feedback System: An optimized processing pipeline ensures minimal end-to-end delay from image capture to robot action execution, achieving true real-time synchronization.

TonyPi's pose control offers more than instant interactive fun. Through fully open-source Python code, it opens the door to learning about robotic motion control and computer vision.

You can start with the pose detection algorithm, diving deep into how to use the 33 human keypoint coordinates extracted by MediaPipe to calculate joint angles and limb orientation, thereby understanding the digital representation of human posture. Further, through practical examples, you'll learn how to map these joint angles into servo control commands, grasping core robotics kinematics knowledge involving coordinate transformation and range-of-motion limits.

Building on this, you can study low-latency image processing and real-time control logic to master key techniques for building highly responsive interactive systems, gaining valuable experience in real-time system design and optimization. Even more openly, you can freely modify pose recognition thresholds, design new action sequences, or even develop a complete "robot dance imitation system," fostering comprehensive innovation skills from algorithm design to mechatronic system integration.

TonyPi perfectly combines advanced pose recognition technology with an open-source learning platform. As you "command the robot to mimic you," you gain an intuitive understanding of how human motion is digitally parsed and transformed into precise robotic movement. It is not only a window into cutting-edge human-robot interaction but also a hands-on platform for learning pose detection, motion control, and robot programming.