While advancements in AI dominate the headlines, the commercial success of humanoids will depend on creating a combination of highly optimized electro-mechanical humanoid system architectures and AI processing units. AI models will need to simultaneously process multiple streams of data from a distributed set of image sensors, synchronize data from dozens of low-speed sensors, and coordinate and control dozens of actuators—all while operating from a limited power source. And of course, this needs to be achieved at scale while optimizing for cost. To help address these challenges MIPI Alliance recently established a Physical AI Birds of a Feather (BoF) group to examine how MIPI’s current portfolio of standardized embedded interfaces can support humanoid architectures. The group aims to analyze the requirements of the complete humanoid system architecture to identify where existing MIPI interfaces and potential future solutions can not only simplify system design, but also optimize performance, lower costs, and offer ecosystem benefits.
How a shift to centralized compute could be key to optimizing humanoid architectures
Initial humanoid system designers have leveraged architectural concepts from several adjacent industries, including industrial robotic and automotive systems, which provided a source of well-engineered control systems and components. These architectures typically distribute processors and microcontrollers throughout the system, with local processors managing each separate subsystem within the architecture.
While the use of distributed models served as a useful basis for initial proof-of-concept humanoids, next-generation humanoid systems will likely see greater adoption of highly optimized centralized architectures that leverage the latest computing technologies. In these systems, powerful SoCs and NPUs are leveraged to analyze multiple camera streams, perform multi-mode sensor fusion, and execute sophisticated AI models in real time. To achieve this, a powerful centralized processor is used to consolidate environmental perception, scene understanding, and task planning, negating the need to employ localized distributed control systems.
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The shift to a more centralized compute architecture offers several advantages, such as reducing component count, lowering power consumption, simplifying software development, and enabling sensor data to contribute to a single, coherent model. That said, the use of localized processing may still play a role in some ultra-low latency subsystems, but many future humanoid systems are likely to adopt highly centralized architectures where a central processor performs most perception and planning.
How communications can become a system bottleneck
With a shift to centralized compute architectures, high-speed, low-latency communication interfaces become a key design requirement. A typical humanoid system incorporates up to 10 cameras, plus inertial, force and torque sensors, tactile arrays in hands, microphones, battery management systems, and as many as 30-60 active joints.
These components generate huge volumes of critical data that must be transported, synchronized, and processed in real time by the central processor, and bandwidth alone is not sufficient to solve the connectivity challenge. Enabling a centralized compute model requires accurate synchronization between sensors, with vision, inertial sensing, tactile feedback, force measurements, and joint-position data arriving with predictable latency and accurate timestamping to be fused into a consistent representation of the robot and its environment. As sensor counts increase, efficient and reliable data transport is equally critical to raw interface bandwidth.
Use of legacy interfaces can exacerbate design constraints
Today’s prototype humanoids often leverage legacy industrial and automotive communication interface technologies such as EtherCAT and CAN-FD because they address similar design challenges in adjacent industries, offer deterministic performance, and benefit from mature software ecosystems. However, the requirements that drove the creation of these interfaces differ from the requirements driving next-generation humanoid architectures. For example, industrial communication interfaces are optimized for long cable runs and communication between a vast array of different distributed controllers, each with a permanent power supply.
Humanoid robots present a very different set of requirements, including shorter communication distances, battery operation, harsh EMC requirements, and more stringent cable and connector requirements, with every gram of weight influencing energy consumption, packaging, and mechanical design. In addition, the type of data traffic in humanoids differs from legacy robotic systems, with synchronized sensor data moving to centralized AI processors rather than across a widely distributed system. The challenge is no longer simply deterministic networking of system components, but delivering high-bandwidth, low-latency communication while minimizing power, wiring complexity, and physical size.
This does not mean the use of legacy industrial networking technologies will disappear, but simply highlights that such technologies may not represent the optimal solution for all communications needs within next-generation, more centralized humanoid architectures.
Highly optimized embedded interfaces enable new humanoid architectures
The MIPI Alliance, over the past two decades, has developed interfaces for the mobile industry that address communication challenges similar to those now emerging in humanoid systems. Smartphones combine multiple cameras, displays, microphones, storage devices, and sensors into compact battery-powered systems, where bandwidth, power consumption, EMC, thermal performance, and physical integration are tightly constrained.
These requirements mirror those of next-generation humanoids, where:
- High-speed camera interfaces must transport synchronized image streams with minimal overhead.
- Low-power sensor interfaces must aggregate numerous devices while maintaining deterministic timing.
- Efficient storage interfaces must reduce AI model times.
- Standardized security frameworks are required to protect sensor data integrity and ensure system safety.
The use of standardized interfaces offers benefits beyond electrical performance, bringing semiconductor vendors, sensor manufacturers, AI developers, software companies, and humanoid original equipment manufacturers (OEMs) into a cohesive ecosystem. Interface standards also provide proven silicon IP, software support, and validation tools, increasing supplier interoperability and reducing integration effort.
Over the past decade, MIPI’s embedded interfaces have been used in adjacent markets, including PC, automotive, industrial, consumer electronics, and IoT. Humanoid architectures could similarly use MIPI’s standardized interfaces to meet their bandwidth, latency, and power requirements for vision, sensing, storage, audio, and control systems while maintaining platform interoperability.
The Physical AI BoF group is currently evaluating MIPI’s existing specifications—including CSI-2 for vision, I3C for sensor connectivity, DSI-2 for displays, SoundWire I3S (SWI3S) for audio, UFS storage based on M-PHY and UniPro, and security frameworks—to support future humanoid architectures and identify needed interface developments.
Adoption of standard interfaces allows developers to focus on product differentiation
The first generation of humanoids has demonstrated what recent advancements in AI can accomplish. The next generation of commercially viable humanoid products will likely be defined by more efficient system architectures and scalability. The use of standardized embedded interfaces could enable developers to optimize their system designs, leverage a broad semiconductor ecosystem, and, most advantageously, allow them to focus their time on developing innovative capabilities that differentiate their products from the competition rather than solving lower-level connectivity challenges.
MIPI Alliance’s Physical AI BoF group, focusing on humanoid system interfaces, is open to everyone in the ecosystem.
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