At the 2026 World Robot Conference, a chip company's technical lead revealed that monthly shipments were about 10,000 units last year and have grown to 100,000 this year. The exhibition floor showcased various humanoid, service, and industrial robots whose internal chips are gradually moving from behind the scenes to center stage. Chips are the core foundation that enables robots to 'see, hear, touch, and move.'
The industry overall shows the characteristic of 'hardware racing ahead, intelligent brains catching up.' Unconverged technology routes, hard-to-balance power and cost, and unverified commercialization remain challenges across the whole chain. A complete humanoid robot's chip system is divided top-down into three layers — 'brain, cerebellum, perception' — that work together to complete the whole process from environmental perception to action execution.
The perception layer builds stereoscopic sensing through multiple sensors. Infineon's booth showed a flexible material as thin as plastic wrap; a light press made the pressure curve on a nearby screen jump immediately. The solution applies not only to robot dexterous hands, but also to humanoid robot foot pressure-balance control, smart running-shoe posture recognition, companion-pet contact interaction, and automotive-interior anti-pinch detection, among diverse scenarios.
The control layer handles joint control and real-time response, directly determining the precision and reliability of movements. As robot joints miniaturize and companion robots lighten, high chip integration has become a core trend. GigaDevice brought two high-integration MCUs for robot joints: one, the GD32F50MxxG series based on the Arm Cortex-M33 core, runs at 252 MHz and integrates a self-developed three-phase gate driver and high-bandwidth op-amp, greatly simplifying peripheral circuits; the other flagship GD32H77R series is deeply optimized for bus communication and integrates an EtherCAT slave controller with a DC synchronization cycle of 62.5 microseconds, improving multi-joint synchronous control. Both use compact QFN80 8x8 mm and BGA169 9x9 mm packages, leaving more board-layout margin within limited joint space.
Beneath the hype, the coordinated development of chips and the robotics industry still faces three core challenges. First, a gap in computing-power supply — the 'brain' cannot keep up with the 'body's' iteration. Perception and execution hardware is now relatively mature, but algorithm models cannot yet fully process complex perceptual signals. Second, power and performance are hard to balance; the power pressure from rising computing power is a problem end-side robots must face. Unlike cars, robots have limited battery capacity, and high power consumption directly cuts endurance. Finally, commercialization remains unverified. Embodied intelligence is in a special stage where research, development, and commercialization run in parallel — more complex than autonomous driving, so the industry's maturity cycle will not be shorter. The core bottleneck has shifted from 'can it be built' to 'can it really be used in scenarios,' and component lifespan and the human-replacement cost ratio are questions commercialization must answer.
