Humanoid robots utilize a variety of microcontroller units (MCUs) and microprocessors (MPUs) depending on their complexity, real-time control needs, and AI processing requirements. Here are some commonly used MCUs/MPUs in humanoid robotics:

1. Real-Time Control MCUs (Low-Level Motor Control)
These handle servo control, sensor interfacing, and real-time tasks:

STMicroelectronics STM32 (ARM Cortex-M)

• Popular series: STM32F4, STM32H7 (high-performance)
• Used in: Small humanoids, robotic joints, sensor hubs

Texas Instruments (TI) Tiva C (ARM Cortex-M)

Example: TM4C123 (used in academic/research robots)

ESP32 (Xtensa LX6, Wi-Fi/BLE)

Used for IoT-enabled robots and low-cost prototypes

Raspberry Pi RP2040 (Dual-core Cortex-M0+)

Used in hobbyist humanoids for PWM/servo control

2. High-Performance MPUs (AI, Vision, Decision-Making)
These run Linux/ROS and handle complex tasks like SLAM, vision, and AI:

NVIDIA Jetson Series

Jetson Orin (AI supercomputer for humanoids like Optimus, Figure 01)

Jetson Xavier NX (used in research robots)

Intel x86-based Processors

Intel Core i7/i9 (for heavy AI workloads)

Intel Atom (low-power companion CPUs)

Qualcomm Snapdragon (ARM-based AI acceleration)

Used in some consumer-oriented humanoids

AMD Ryzen Embedded (for high-performance robotics)

3. Specialized AI Accelerators & Co-Processors

• Google Edge TPU (for fast ML inference)
• Intel Movidius Myriad X (vision processing)
• Tesla Dojo (custom AI chip for Optimus robot)

4. ROS-Compatible & Research Platforms

• BeagleBone Black (TI Sitara AM335x)
• NVIDIA Jetson + STM32 combo (common in research robots)
• Raspberry Pi 5 (as a mid-level controller)

Examples in Commercial Humanoids:

• Tesla Optimus → Custom AI chips (Dojo) + likely NVIDIA Orin
• Boston Dynamics Atlas → Custom real-time controllers + Intel/AMD CPUs
• Unitree H1 → Jetson Orin + STM32 for motor control
• Agility Robotics Digit → Xilinx FPGAs + ARM-based MPUs

Trends in Humanoid Robot MCUs:

1. ARM Cortex-M & R cores dominate low-level control.
2. NVIDIA Jetson/Orin is becoming standard for AI.
3. Custom AI accelerators (like Tesla Dojo) are emerging.
4. FPGAs (Xilinx/Intel) are used for high-speed motor control.