Embodied Artificial Intelligence (& Humanoid Robot) MCU Research Report, 2026

Research on Humanoid Robot MCUs: Evolution from General-Purpose Control to High-Value Dedicated Chip Solutions Integrated with Edge AI Functions

MCU (Microcontroller Unit) refers to a compact integrated circuit that integrates a central processing unit (CPU), memory (RAM, ROM/Flash), input/output interfaces (I/O), timers/counters, analog-to-digital converters (ADC) and other peripherals onto a single chip. It is also known as a single-chip microcomputer.

Currently, most MCUs adopted by embodied artificial intelligence (EAI) are general-purpose MCUs. However, as performance requirements and scenario applications for EAI increase, dedicated MCUs have been launched to ensure stable operation of EAI across various scenarios. In the future, such dedicated MCUs will encroach upon the market for general-purpose MCUs and be widely applied in EAI manufacturing.

MCUs are not deployed in a "single-chip centralized control" mode on robots. Instead, they are embedded in a distributed manner across all system layers to undertake perception, control, communication, execution and other tasks. They play vital roles in the core control system, joint controller system, perception system, power management system and communication system of EAI.

Core Control System: As the "nerve center" and "cerebellum", MCUs are embedded in full-body joints, perception and communication modules in the form of distributed nodes, undertaking full-link functions ranging from real-time motion control to system safety.

Joint Controller System: MCUs are widely deployed in various joints as execution units to complete local closed-loop control of individual joints and guarantee accurate movement execution. In addition, due to the unique characteristics of dexterous hands, MCUs of highly integrated small-size specifications are embedded in finger joints to achieve full coverage of degrees of freedom.

Perception System: MCUs undertake distributed real-time processing tasks within the perception, decision and execution closed loop to enable such functions as sensor data sensing and collection, multi-modal signal preprocessing and fusion.

Power Management System: MCUs are mainly used to realize battery status monitoring, energy consumption scheduling and safety protection.

Communication System: MCUs allow for instruction circulation and synchronous interconnection among the "cerebrum-cerebellum-joint-end effector".

Encoder Module: Responsible for converting mechanical motion into high-precision, low-latency electrical signal feedback to support closed-loop control.

IMU (Inertial Measurement Unit) Module: The core component for robots to perceive their own posture, movement, acceleration and angular velocity. MCUs output motion posture by reading data and running algorithms.

Specialized Evolution Trend of Joint MCUs Amid Technical Route Differentiation of Humanoid Robot Dexterous Hands

As primary joint modules of robots, dexterous hands will mainly follow five development trends in the future:

According to the evolution trends of dexterous hands, five core trends have formed at the MCU level: small-size packaging, high-performance real-time processing, I3C bus communication, NPU edge AI, and functional safety certification.

Trend 1 - Miniaturization & High Integration: As dexterous hands tend to have higher degrees of freedom, internal hand space becomes extremely constrained, driving MCUs toward smaller sizes and higher integration. In packaging, the industry is shifting from BGA to WLCSP (4X4mm, even smaller). GigaDevice GD32G553 has realized 4X4mm WLCSP; single-chip multi-motor control is targeted, with the "single-chip integrated all-in-one solution" as the ideal direction. NXP i.MX RT1180 can directly drive up to 6 brushless coreless motors via a single chip.

Trend 2 - Enhanced High-Performance Real-Time Processing Capability: Required main frequency evolves from the current 200MHz to 600MHz or even 1GHz; multi-core architectures will find wide application and become mainstream standard configurations. NXP i.MX RT1180 adopts a dual-core architecture (240MHz M33 + 800MHz M7); integrated hardware accelerators including FPU, DSP, TMU (Trigonometric Math Unit) and FAC (Filter Algorithm Accelerator) become a must.

Trend 3 - New Bus Communication Architecture I3C: The I3C bus topology connects multiple servo nodes and tactile sensors, while externally linking to the robot system bus via EtherCAT, CAN and RS485, advancing dexterous hand systems toward higher integration, higher performance and wider application scenarios.

Trend 4 - Edge AI Computing Delegated to MCU: Dexterous hands are evolving into independent intelligent subsystems with dedicated domain controllers supporting edge AI for gesture recognition, object detection, slip prediction and other tasks, without relying on cloud computing power.

Trend 5 - Specialization and Functional Safety Certification of MCUs: To expand market size, specialized MCUs become a trend and can effectively reduce overall system BOM costs.

I3C Distributed Bus Architecture Adopted by Dexterous Hand MCUs Facilitates High Integration of Dexterous Hand Systems

Compared with traditional I2C, the I3C bus delivers outstanding advantages for dexterous hand systems including high-speed communication, simplified wiring and hardware design, dynamic device management and real-time response.

NXP innovates in internal communication modes for dexterous hands with an I3C-based local bus topology. This architecture uses i.MX RT1180 as the palm main control MCU and MCX A132 as finger joint control MCUs, connecting multiple servo nodes and tactile sensors via the I3C bus, while externally linking to the robot system bus through EtherCAT, CAN and RS485.

NXP i.MX RT1180 serves as the palm main control MCU, featuring a dual-core architecture (240MHz M33 + 800MHz M7) with high-performance processing capabilities. It integrates 2 I3C interfaces to connect multiple servo nodes and sensors, supports multiple industrial communication protocol interfaces such as EtherCAT and CAN-FD, and provides abundant PWM, ADC and encoder interfaces. A single chip can directly drive up to 6 brushless coreless motors.

NXP MCX A132 is deployed at finger joint servo nodes and tactile sensors. This MCU features small-size packaging suitable for embedding into finger modules, integrates 1 I3C interface for high-speed communication with the main controller, embeds a 16-bit ADC for high-quality analog signal collection from tactile sensors, and supports the IEC 61508 SIL2 functional safety self-test library to meet future functional safety requirements of humanoid robots.

Edge AI-Integrated Dexterous Hand MCUs Enable Execution of End-effector Model Algorithms

Shift EAI computing power down to robot end effectors (dexterous hands) to realize local closed loops for tactile sensing, force control and decision. Partial multi-modal perception tasks of EAI run on-device inference within dexterous hands instead of transmitting data back to the cloud.

STMicroelectronics has launched gesture recognition and control systems based on STM32N6 and STM32MP257 to match market demand and internal technical evolution. This system is composed of three core parts: Perception part based on STM32N6 for gesture recognition and data collection; PLC part based on STM32MP257 for data conversion and processing tasks; motion control part based on STM32G431 for motion control and gesture following of dexterous hands.

The NPU inside STM32N6 runs sophisticated gesture recognition models. Paired with the VD66GY high-sensitivity color image sensor, it accurately captures and recognizes 21 key points of hand gestures. The STM32MP257 PLC demonstration board processes this data and controls 15 servo motors in a dexterous hand, enabling real-time, precise gesture responses. STMicroelectronics showed its outstanding integration capability in combining microcontroller technology with advanced sensors to achieve real-time gesture recognition and control.

STM32N6 microcontroller integrates ST's powerful Neural-ART accelerator NPU with processing capacity up to 600 GOPS and ultra-low power consumption of 3 TOPS/W. It adopts a Cortex-M55 core with a main frequency of 800MHz and adds 150 DSP Vector Extension (MVE) instruction sets, delivering exceptional computing performance for visual data processing to guarantee efficient and accurate gesture recognition.

Humanoid Robot MCU Market Trend: Full-Stack Chip Solutions + Specialized EAI MCUs

As the EAI market keeps expanding, MCU vendors no longer rely on single MCU to satisfy robot requirements. Instead, they coordinate multi-product lines of "MCU + analog + storage + sensing + communication" to cover the full link of perception, control, drive, communication and safety, while providing reference designs and ecosystem support.

In the chip-enabled robot hardware segment, GigaDevice has built a complete product matrix covering control, storage and analog to deliver full-stack chip support for humanoid robots. Its high-performance GD32 MCUs are applied for real-time multi-joint motion control and system scheduling; its Flash memory delivers high-speed, high-reliability data storage guarantee for decision computing; analog chips cover key links such as sensor signal conditioning, motor drive, and power management, empowering robotic systems across the entire chain from perception and decision to execution.

Overall, GigaDevice GD32 MCUs can coordinate with GD30DR series driver chips, paired with LDO, DC-DC, PWM and protection circuits to build complete, reliable robot motor control solutions fully serving joint drive and servo systems.

Customize Specialized EAI MCUs Based on Characteristics of Each EAI System

The industry is shifting from general-purpose MCUs to highly integrated, real-time, functional safety dedicated MCUs customized by robot component (joint/dexterous hand/sensing node), supported by coordinated full-stack chips. This transformation facilitates high integration and functional safety certification, precisely resolving space, synchronization and mass production challenges of joints and dexterous hands while lowering BOM costs and development barriers. More vendors will prioritize R&D of specialized EAI MCUs in the future.

HPMicro has developed HPM53M1, a dedicated MCU exclusively built for CAN communication and motion control of robot joints. Inheriting HPMicro's consistent strengths of powerful computing power, high real-time performance and high reliability, HPM53M1 integrates high-voltage pre-drivers and multi-channel operational amplifiers for the first time, making it the MCU with the most powerful integrated pre-drive performance available globally.

HPM53M1 adopts a high-performance RISC-V core running at 480MHz, supporting double-precision floating-point calculation and robust DSP expansion capabilities to easily handle complex control algorithms. In terms of storage architecture, it is equipped with 1MB Flash memory and 288KB SRAM, supplemented by 16KB high-speed cache (I/D Cache) and up to 256KB zero-wait instruction and local data memory (ILM/DLM). It carries high-performance analog peripherals including 2-channel 16-bit ADC (2MSPS), 2 OPMAPs configurable as PGAs, and 2-channel 12-bit DAC (1MSPS). It can collect current, voltage, temperature and position sensing signals at ultra-high rates in real time, enabling faster dynamic joint response and qualitative improvements in control precision.