FPGAs for Sensor Processing and Motor Control in Humanoid Robots
How FPGAs Support Humanoid Robots
(Source: Gorodenkoff/stock.adobe.com)
Published August 27, 2026
Robots are proliferating across a wide range of applications, and many are tailored closely to the task they perform. Wheeled robots, for example, can handle goods in warehouses, while industrial robotic arms can weld metal components together. Meanwhile, humanoid robots are gaining traction because of their versatility. They offer the flexibility to perform a variety of tasks while operating autonomously in dynamic environments and mimicking human motion.
To perform their assigned tasks safely while maintaining their own stability around humans, humanoid robots require high-performance hardware and software capable of managing the many sensors and motors that enable them to perceive and act. Field-programmable gate arrays (FPGAs) are well-suited to serve as the brains in humanoid robotic applications (Figure 1). FPGAs can flexibly and deterministically process multiple sensor inputs while controlling the dozens of motors that give robots human-like dexterity.
This blog examines how FPGAs support sensor processing and motor control in autonomous humanoid robots.
Figure 1: A humanoid robot’s brains (right) process perception inputs (center) and control scores of motors (left). (Source: Microchip Technology)
Perceiving the Environment
Humanoid robots use a variety of sensors to map their work environment and determine how to complete their assigned tasks. Like other robotic systems, humanoid robots may rely on LiDAR for 3D mapping, embedded vision sensors for object recognition, ultrasound for collision avoidance, and radar. Radar is particularly valuable because it can operate reliably in adverse weather and low-light conditions while simultaneously measuring a target's distance and velocity.[1]
While these sensor modalities apply to robots in general, humanoid robots face additional challenges. For example, a wheeled warehouse robot can navigate to a programmed location and then lock its wheels, creating a stable platform for its next task, such as retrieving an item scheduled for shipment. By comparison, a humanoid robot must constantly maintain whole-body awareness. To do so, it relies on proprioceptive sensors to monitor body orientation, finger pressure, joint rotation, limb position, and foot placement. These sensors can include inertial measurement units (IMUs) that measure acceleration and angular velocity, force and torque sensors that monitor weight distribution on each foot, and rotary encoders that track joint angle and velocity.
In general, sensor outputs in isolation are insufficient for the successful autonomous operation of a humanoid robot. As a result, robots rely on sensor fusion to process multiple sensor outputs simultaneously. Sensor fusion requires handling multiple interface protocols, ranging from vision-centric CoaXPress to more general-purpose serial peripheral interface (SPI), inter-integrated circuit (I2C), universal asynchronous receiver/transmitter (UART), and general-purpose input/output (GPIO), as well as Mobile Industry Processor Interface (MIPI) interfaces.
The objective is to consolidate these diverse sensor data streams in multiple formats at the edge, minimizing latency and enabling the robots to react safely in real time. To enable real-time sensor fusion and decision-making based on combined sensor data, FPGAs consolidate multiple data streams in various formats into a consistent output suitable for artificial intelligence (AI) inference workflows. NVIDIA’s Holoscan multimodal computing platform further supports this process by bridging sensors with edge servers and GPU-accelerated systems for real-time AI processing.[2]
Real-Time Motion Control
Achieving safe, stable, and reliable motion control in autonomous robots is one of the biggest challenges in robotics. In response to external commands and sensor inputs, a humanoid robot's processor must control scores of motors that enable the robot to reach its destination and maintain stability while performing its assigned task. A humanoid robotic hand illustrates this complexity. Each finger may contain two to four motors, providing up to 20 degrees of freedom across the hand. Unlike a stationary robotic arm or a simple wheeled autonomous robot, a humanoid robot requires a wide range of motors working in concert with vision, pressure, strain, and other sensors to operate safely around humans while performing the object handling necessary to complete its assigned task.
The processor controlling these motors must integrate vision, radar, ultrasound, and LiDAR sensor data with proprioceptive sensor data, including the embedded torque, pressure, and position sensors that each finger may require. The robot must also provide tactile feedback. The typical robot uses brushless DC (BLDC) motors to maximize efficiency and performance. These motors rely on fast closed-loop control systems that continuously adjust speed, position, and torque based on sensor feedback.
The robot will also require fast, real-time control with tight synchronization across its multiple joints and limbs, enabled by time-sensitive networking (TSN).[3] Based on sensor inputs, the controller must derive stability-related quantities such as center of mass, center of pressure, and zero moment point, all of which can help a humanoid robot determine if a fall is imminent, and the realignment necessary to avoid falling.[4]
Controlling many motors in parallel can be challenging with simple microcontrollers because their processing resources are typically shared across control loops. In contrast, FPGAs can implement parallel logic blocks for deterministic, tightly synchronized control of multiple motors.
FPGAs for Humanoid Robots
Microchip Technology offers its PolarFire® FPGAs to provide a head start for humanoid robot designs. Compared with functionally equivalent static random-access memory (SRAM)-based FPGAs, they can deliver up to 50 percent power savings. A PolarFire FPGA can implement an Ethernet Sensor Bridge that connects directly to the NVIDIA Holoscan sensor-processing platform, accelerating the development and deployment of AI-based machine-vision applications.
As shown in Figure 2, the bridge can acquire touch sensor data and send it to a GPU or SoC for processing. It then relays BLDC motor-control commands to the BLDC motors in the robot’s hand, with one FPGA capable of controlling up to 40 motors simultaneously, providing the processing power needed for complex humanoid designs with many degrees of freedom. Other features include secure-boot functionality with firmware-over-the-air (FOTA) updates and compatibility with the Robot Operating System 2 (ROS 2), which simplifies integration into modern robotics development environments.[5]
Figure 2: A PolarFire FPGA can process touch-sensor signals and control multiple BLDC motors. (Source: Microchip Technology)
Conclusion
Autonomous humanoid robots are complex systems that must continuously process data from numerous sensors while coordinating the many motors responsible for movement, balance, and manipulation. FPGAs provide the deterministic processing, sensor fusion capabilities, and parallel motor control required to support these demanding real-time workloads. FPGAs, in conjunction with GPUs or SoCs, can help meet key performance requirements for autonomous humanoid robots by supporting deterministic sensor processing and fast motor-control loops while simplifying key aspects of system design.
[1]https://www.microchip.com/en-us/solutions/industrial/smart-embedded-vision
[2]https://www.nvidia.com/en-us/edge-computing/holoscan/
[3]https://www.microchip.com/en-us/solutions/industrial/fpga/polarfire-fpgas-for-the-industrial-edge
[4]https://ieeexplore.ieee.org/abstract/document/1325327
[5]www.ros.org
Author
Rick Nelson is a technical journalist who has served as executive editor of Test & Measurement World, chief editor of EDN, and executive editor of EE-Evaluation Engineering. He has also contributed to publications including Vision Systems Design and Electronic Design, and he has participated in many live panel discussions and webcasts. Rick has also held systems-engineering and product-development positions at General Electric and Litton Industries. He received his B.S.E.E. degree from The Pennsylvania State University.