Humanoid robots are approaching their "coming of age."
In recent years, we have grown accustomed to seeing robots dance, perform backflips, or conduct simple educational demonstrations at major trade shows. However, as the industry enters a more mature phase, the focus is rapidly shifting from "cool motion displays" to "stable, real-world operation in factory environments for 24/7 use."
The gap between performance and practicality is harder to bridge than imagined. When robots enter real industrial scenarios, facing complex all-terrain conditions, strong wind interference, and intense vibrations caused by the startup of numerous internal motors, traditional sensor solutions begin to fail.
Recently, global semiconductor giant Analog Devices Inc. (ADI) achieved its first-ever quarterly revenue of $4 billion, driven by strong performance in the industrial and data center sectors, with the industrial business accounting for 49% of this total. During the China International Industry Fair (CIIF 2026), Zhao Yanhui, Director of ADI's Asia-Pacific Microelectromechanical Systems (MEMS) Product Line, discussed Inertial Measurement Units (IMUs) to highlight the most critical hidden pain points in humanoid robot hardware: vibration interference and long-term stability.
Image | Joint Module; Source: ADI
The Overlooked Vibration Rectification Error (VRE)
In indoor environments or scenarios where satellite signals are limited, robots rely primarily on IMUs for dead reckoning and global attitude control. The accuracy of the gyroscope is crucial here, as its errors are integrated three times, directly determining the accuracy of navigation direction.
Many engineers habitually place IMUs statically on a desk to collect data. Seeing minimal fluctuation, they assume the accuracy is sufficient. However, these static metrics often fail instantly under actual dynamic operating conditions.
Zhao Yanhui explained that when a robot moves or its motors spin at high frequencies, complex vibration interference occurs. This introduces a critical metric: Vibration Rectification Error (VRE). Due to the presence of Earth's gravity and the asymmetry of MEMS structures, when the amplitude of high-frequency vibrations becomes too large, measurement in one direction experiences clipping distortion. The clipped signal, after processing through a low-pass filter with integration characteristics, generates an unavoidable DC bias.
This DC bias causes the robot to misjudge its orientation, and the longer it operates, the greater the accumulated error becomes. This is a fundamental physical pain point that backend algorithms or software filters absolutely cannot resolve.
Side Effects and Technical Compromises of Traditional Anti-Vibration Solutions
To address deviations caused by vibration, the industry has historically employed various compromise methods. However, while solving old problems, these approaches often come with significant "side effects."
The first is real-time compensation via software and AI. This method attempts to instantly capture the magnitude of vibration and apply non-linear compensation to the gyroscope. However, in actual operating conditions, the same acceleration can result from entirely different combinations of vibration frequency and amplitude, causing VRE variations to be highly random. While theoretically feasible, software compensation incurs extremely high computational costs, often requiring additional expensive GPU resources.
The second is adding damping materials such as rubber. This physical buffering method is commonly used around robot joints or sensor modules. However, rubber materials degrade easily in high-temperature environments. Especially when internal motors in robots or drones generate heat, equipment may become unstable and oscillate after running for some time. Furthermore, if damping materials are unevenly applied, they can directly disrupt the orthogonality of multi-axis sensors, leading to signal coupling between axes and resulting in misjudgments. More seriously, physical damping extends signal transmission time, increasing response latency and thereby reducing overall real-time control performance.
The third is relying solely on motor encoders for estimation. Some solutions choose not to use external sensors, estimating posture only based on position data and motion signals from multiple motors. However, this indirect estimation has two fatal flaws: once a motor or joint slips on complex terrain, the odometry generates cumulative errors that cannot be corrected in a timely manner; simultaneously, deriving posture step-by-step from multi-stage motor signals results in significantly higher response latency compared to direct sensor measurements, making it difficult to ensure real-time stability during high-dynamic movements.
True Chip-Level High-Performance IMU
Facing these pain points that system-level solutions cannot independently resolve, ADI launched a new product in 2026: the ADIS16607 chip-level Inertial Measurement Unit (IMU).
Image | ADIS16607 (inside red box); Source: EEFChina
Zhao Yanhui pointed out that unlike previous "module-level" products that integrated separate accelerometers, gyroscopes, and processors within a BGA package, the ADIS16607 is the first to fully integrate ADI's accelerometer IP, gyroscope IP, signal processing IP, and processor IP onto a single chip, maintaining ultra-high precision while compressing the size to 6.9mm × 6.9mm.
Each axis inside this device adopts an innovative architecture featuring four MEMS motion cores. When the robot faces severe external vibration interference, this physical architecture functions like an instrumentation amplifier, suppressing external environmental interference as common-mode signals while amplifying true motion as differential-mode signals. This allows it to maintain excellent zero-point stability even under intense swept-frequency vibration conditions.
Zhao Yanhui emphasized that industrial-grade humanoid robots need to operate continuously for 24/7 or undergo long-term idle storage. The ADIS16607 has passed rigorous reliability assessments, including High-Temperature High-Pressure Accelerated Aging Tests (HTHP), Temperature Cycling Tests (TCT), and Highly Accelerated Stress Tests (HAST). Its full-temperature-range calibration before shipment ensures the sensor possesses extremely high long-term stability, allowing customers to achieve 0.1-degree pitch and roll control accuracy without complex post-production anti-vibration design or secondary compensation.
IMU Selection and Architecture Deployment for Different Parts of Humanoid Robots
Since different parts of a humanoid robot experience varying motion states and force conditions, IMU selection and key metric priorities differ accordingly.
In the head system, the IMU works in coordination with the lens for visual navigation. The key factor here is time synchronization; the device must support external synchronization triggering to ensure strict alignment between IMU data frames and camera image frames, providing low-latency continuous motion compensation between high-frequency visual frames.
In the waist and torso area, the IMU is primarily responsible for global attitude detection, fall prevention, and gait planning. Although isolated from external shocks by leg joint damping, this part determines the overall direction. Therefore, devices here prioritize zero-point stability and long-term repeatability to minimize angle drift during prolonged operation.
At the feet and leg extremities, sensors are in direct contact with the ground, facing frequent mechanical impacts and high-frequency terrain vibrations. Sensors deployed here require a large gyroscope range (e.g., ±2000°/s), ultra-high resistance to mechanical shock, and extremely low Vibration Rectification Error (VRE) to accurately detect ground flatness through vibration feedback.
Near the robotic arms and joints, space and load capacity are severely restricted, and motor startups are frequent. This area requires extremely compact chip-level IMUs, or separately installed high-bandwidth accelerometers (e.g., 16kHz) to directly measure grasping actions, eliminating estimation biases caused by joint slippage and enhancing the system's real-time response speed.
Additionally, Zhao Yanhui mentioned that beyond conventional attitude control, high-bandwidth accelerometers (such as the ADXL383) can be deployed near joints for equipment health monitoring. When internal components age rapidly, screws loosen, or bearings wear out, the equipment's vibration waveform changes, manifesting in harmonics above the 10th order. By performing time-domain and frequency-domain analysis (FFT) on 16kHz wide-bandwidth data, early warnings can be issued before component failure, enabling predictive maintenance to ensure stable 24/7 operation of factory equipment.
Conclusion
Moving humanoid robots from laboratory demonstrations to actual industrial applications essentially requires resolving various physical interferences during motion. While algorithms and software can correct some deviations in later computations, under actual operating conditions involving high-frequency motor vibrations, continuous temperature changes, and hard ground impacts, if sensors experience signal clipping due to physical saturation, software and algorithms mathematically cannot recover this lost information.
Therefore, to solve control distortion and unstable gait in humanoid robots, efforts must start from the underlying hardware and chip design. By using a quad-core architecture to cancel out vibration interference at the hardware level, employing ceramic packaging to isolate stress changes caused by environmental temperature and humidity, and utilizing single-chip integration to reduce volume and minimize axial deviation, high-performance MEMS IMUs can output true and accurate motion data from the source. This hardware-level stability not only provides reliable input data for upper-layer navigation and gait control algorithms but also serves as the foundational support for humanoid robots to operate stably across all terrains for extended periods.