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Humanoid robots expose different hardware, software APIs, units, sensor and actuator conventions, and joint names or indexes. As a result, software built for one robot often needs substantial adaptation before it works on another. Standardized interfaces can reduce that repeated integration work by giving developers shared contracts for specific layers, while leaving robot-specific differences to adapters. They can make reuse easier; they do not make every robot interchangeable, and the available evidence does not establish an industry-wide time or cost savings figure.
How interfaces affect humanoid development
An interface is the contract through which software or components exchange information or commands. Depending on the layer, that contract might specify a mechanical mount, electrical connection, network transport, software API, module information model, task vocabulary, or safety function. Those layers solve different problems: a shared connector does not standardize a control API, and a shared software API does not make two robots’ physical behavior identical.
Without shared contracts, an application may depend on a vendor’s API and assumptions about sensors, actuators, units, joint names, or indexes. Porting it means first understanding how the target robot represents state and accepts commands, then adapting the application. The TOUGH paper describes this portability challenge and proposes a transportable API and user interface using ROS topics to read robot state and sensor data and send desired commands (Jagtap et al., “Transportable open-source application program interface and user interface for generic humanoids: TOUGH,” first published May 6, 2020).
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A shared contract lets developers write some capability logic against common inputs and outputs rather than a single robot’s private conventions. A platform-specific adapter can then translate between that generic logic and the robot’s hardware or software layer. This shifts some repeated integration work toward adapters and reusable contracts, making it easier to reuse code, compare modules, choose components, and maintain larger systems.
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Adapters still have work to do. They may need to reconcile units, coordinate frames, timing, joint mappings, safety behavior, and vendor-specific capabilities. A common interface does not by itself prove that a component is safe, compatible, or performant on every robot.
A bounded example: tactile reflexes on NAO and iCub
A 2016 study of the Cross-Platform Tactile Capabilities Interface abstracted arm actuators and tactile skin sensors. It reports implementing protective withdrawal reflexes on both NAO and iCub with the same high-level source code, translating generic capability code through platform-specific layers (“A Cross-Platform Tactile Capabilities Interface for Humanoid Robots”).
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The result demonstrates portability for the studied arm and tactile capabilities, not for every whole-body behavior or every humanoid platform. It also does not quantify general development-time or cost savings.
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Where ROS 2 fits
Open Robotics describes ROS 2 as an open-source software development kit intended to provide a standard software platform from research and prototyping through deployment and production (ROS 2 brochure). A common software layer can support shared development practices, but ROS 2 alone does not make hardware, timing, safety behavior, or the meaning of commands identical across robots.
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What current standards and projects cover
There is no established, complete standard in the cited work that covers all humanoid hardware and software interfaces. Instead, current efforts address different layers and have different levels of maturity.
| Effort | Layer and scope | Status and key boundary |
|---|---|---|
| ISO 22166-202:2025 | Information model for service-robot software modules. ISO says it can help builders compare modules from different vendors and integrate them into larger systems; its lifecycle coverage includes design, development, operation, and maintenance. | Published standard. It addresses service-robot modularity, not a complete humanoid standard. ISO standard page |
| ISO/AWI 24112 | Electrical connectivity and interoperability for end-effectors. Its abstract covers end-effectors with nominal power requirements up to 200 W, plus robot-side electrical connection characteristics, designation, marking, and interfacing for safety-related functions. | Approved work item under development. The abstract excludes mechanical requirements beyond electrical connectors, network requirements beyond application-layer communication protocols, and safety performance requirements. Mechanical interfaces are referenced separately as ISO 9409-1 and -2. ISO work item page |
| IEEE P3870 | Proposed reference framework and general requirements for intelligent-control systems in humanoid robots used in digital production and services. Its scope includes foundational resources, runtime, software platforms, intelligent capabilities, application interfaces, and safety-assurance requirements. | Active project, approved September 25, 2026; it is not a published standard. IEEE Standards Association project page |
| IEEE 1872.1-2024 | Robot task knowledge represented through terms, attributes, types, structures, constraints, and relationships. | A task-representation ontology relevant to shared meaning, not a physical connector or robot-control API. IEEE Robotics and Automation Society approved standards |
| IEEE humanoid study group | Examines which existing standards apply to humanoid robots, identifies gaps and roadblocks, and develops a standards roadmap. | Landscape and gap-analysis activity, rather than a complete interface standard. IEEE Robotics and Automation Society study group |
How to assess whether an interface effort will help
When choosing between interface approaches or judging a standard’s relevance, compare them at the layer where your integration problem occurs. A shared task vocabulary, for example, may clarify meaning without resolving electrical or control compatibility.
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- AI Vision Recognition and Tracking. TonyPi's 2DOF head is fitted with an HD camera that provides a wide field of view. It supports a range of AI vision capabilities, including color recognition, target tracking, ball kicking, line following, and MediaPipe-based motion control for interactive AI applications.
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- Layer: Is the need mechanical mounting, electrical power or signals, network transport, a software API, module information, task semantics, or safety functions?
- Scope: Does the interface cover one end-effector, a software module, a robot subsystem, an application interface, or an entire system?
- Maturity: Is it a published standard, an approved work item under development, an active project, a research prototype, or a vendor API?
- Portability evidence: Are there implementations on multiple platforms, and exactly what code or behavior was reused?
- Remaining integration work: Which adapters, unit conversions, coordinate frames, joint mappings, timing constraints, safety assurance, and vendor-specific behaviors remain?
What the evidence can—and cannot—say about speed
The evidence supports a mechanism for reducing duplicated integration: shared contracts let developers reuse some higher-level code and concentrate platform differences at defined boundaries. The NAO/iCub tactile example shows this for a specific capability; the standards work addresses individual layers rather than every part of a humanoid.
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The cited sources do not establish a reliable industry-wide figure for development time or cost saved. The cross-platform case study is not a general savings statistic, so a universal percentage or return-on-investment claim would go beyond the evidence.
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