China's first batch of six national standards in the humanoid robot field was approved and released on September 28, 2026, with unified implementation starting April 1, 2027. The six standards include five parts of the Technical Requirements for Humanoid Robots series and one Specification for Simulation Test Platforms for Humanoid Robots. In simple terms, the first five standards are more like setting requirements for candidates, answering what level the robot body must reach; the Specification for Simulation Test Platforms for Humanoid Robots (GB/T 48316—2026) is more like managing the examination venue and grading rules, answering what tools, conditions, and processes are used to determine if test results are credible.
The standard text in this article is based on the readable text of GB/T 48316—2026; the background of release is based on the National Standard Announcement and relevant public information from the State Administration for Market Regulation. GB/T 48316—2026 can be searched on the National Standards Information Public Service Platform, and the National Standard Announcement query system can confirm the numbers, names, and implementation dates of the six standards.
Table 1: Summary Table of the Six Standards
Data Source: National Standards Information Public Service Platform, compiled by MIR DATABANK
Figure: Web page for the release of the GB/T 48316-2026 standard
Part I: Why Is This Standard Needed?
Table 2: Details of the Standard
Data Source: Specification for Simulation Test Platforms for Humanoid Robots, compiled by MIR DATABANK
The drafting team is quite impressive, essentially an "all-star team": key body manufacturers and state-owned enterprises are all included.
1) A Simple Question: Why Can't Robots Trial-and-Error Infinitely in Reality?
Training humanoid robots to walk, grab cups, or converse with humans is essentially a process of continuous trial and error. However, real-machine trial and error faces two prominent issues: First, it is expensive; the cost of a humanoid robot is not low, and the loss from a single fall is significant. Second, it is slow and risky; a real machine can only run limited scenarios at a time, and human-proximity testing may cause injury to people or equipment. Thus, simulation test platforms allow algorithms to practice and take exams in a virtual world built on computers before being transferred to real machines after passing screening. The industry often refers to this process as "sim-to-real."
2) Pain Point: Inconsistent Quality of Virtual Worlds Across Vendors
Simulation platforms were previously mostly self-built by enterprises or modified based on open-source tools. There was no unified metric for visual fidelity, physical accuracy, or data stability. As a result, the same algorithm might perform well on Platform A but poorly on Platform B, and transferring it to a real machine could yield entirely different results. Without a unified ruler, test results lack credibility. GB/T 48316—2026 aims to solve exactly this problem.
3) Where Does the Simulation Test Platform Sit in the Industrial Chain?
The humanoid robot industrial chain can be roughly divided into upstream hardware, midstream complete machines and software, and downstream applications. The simulation test platform plays three roles simultaneously in the midstream.
Table 3: Role Positioning of Simulation Test Platforms in the Humanoid Robot Industrial Chain
Data Source: Specification for Simulation Test Platforms for Humanoid Robots, compiled by MIR DATABANK
Training data sources generally fall into three categories: real-machine collected data is closer to reality but costly and slow; simulation synthetic data is cheap, fast, and voluminous; internet video data is abundant but has limited compatibility with robot bodies. Although the simulation platform's share in the industrial chain may not be large, in terms of penetration in the R&D stage, it is infrastructure that many manufacturers cannot bypass.
4) Why Did This Type of Standard Form Readable Text First?
Robot body technology routes vary significantly; bipedal and wheeled designs, as well as fully rotational joint and linear joint solutions, each have their proponents. Metrics such as frame rate, latency, error, and log replay for simulation platforms are closer to engineering indicators, making cross-route consensus easier to form.
The industry universally requires synthetic data to supplement real-machine data.
The Ministry of Industry and Information Technology has released the Guidelines for the Construction of the National Humanoid Robot Industry Standard System (2026 Edition) and is promoting scenario-specific urgent standards, open-source communities, and inspection and testing capabilities to facilitate rapid application of standards.
According to Li Jincun, Secretary-General of the National Technical Committee for Robotics Standardization, the six released standards follow a complete technical closed loop "from cognition to action, then to verification." The general principles draw the technical map first, unifying terminology, architecture, and interfaces to solve the industry's "common language" issue.
As reported by CCTV: Environmental perception is responsible for understanding. Drafter Wang Xingxing stated that when a robot approaches a person, speed and power must drop below safety standards, for example, maximum speed should not exceed 0.5 meters per second. Decision planning is responsible for determining the next step. Drafter Quan Qi indicated that command accuracy should be above 95%, and single-action completion time within 1 second. Motion control is responsible for stable movement. Drafter Ren Rongwei introduced that when pushed by rated external force, it must restore stable working status within 1 second. Task operation is responsible for completing the work effectively: success rate for grasping rigid objects above 90%, for deformable objects above 85%, and force operation above 15 kg. Above these five technical requirements, the simulation test platform standard (GB/T 48316—2026) assumes the verification role, unifying where to test, how to test, and whether results are reproducible.
Part II: Four Major Blocks of Content in Standard 48316
Block 1: Basic Definitions
Chapters 1 to 4 define the scope, referenced documents, terms, and abbreviations. The core definition states that the simulation test platform provides testing capabilities for humanoid robot simulation tests, covering algorithm testing during functional module development, functional verification and performance evaluation during system integration, and comprehensive evaluation at the whole-machine level. It normatively references GB/T 48317.1 (not yet released), meaning the platform standard must be read in conjunction with the whole-machine general principles.
Block 2: Platform Architecture, Divided into Three Layers
Chapter 5 divides the platform into three layers: the Core Engine Layer includes the Graphics Engine, Physics Engine, and Agent Engine; the Scheduling Management Layer is handled by the Task Scenario Manager; the External Interface Layer includes the Simulation Sensor Interface, Joint Control Interface, and Multimodal Interaction Interface.
Table 4: Platform Architecture
Data Source: Specification for Simulation Test Platforms for Humanoid Robots, compiled by MIR DATABANK
Graphics Engine: Responsible for "drawing," rendering 3D scenes into images (lighting, shadows, materials);
Physics Engine: Responsible for "calculating," simulating collisions, friction, and gravity to ensure the virtual world conforms to physical laws;
Agent Engine: Responsible for "acting," driving virtual humans and other robots ("NPCs") to interact and converse with the robot under test;
Task Scenario Manager: Overall scheduler, responsible for loading scenes and commanding the three engines;
Three Types of Interfaces: Connect external algorithms, including Sensor Interfaces (virtual cameras, radar, etc.), Joint Control Interfaces (transmitting motion commands), and Multimodal Interaction Interfaces (voice, text, image interaction).
Block 3: Technical Requirements, Quantified Standards
Chapter 6 is the key part of the full text. Here, three terms need to be distinguished: "shall" indicates mandatory compliance, "should" indicates recommendation, and "may" indicates optional choice.
Table 5: Key Quantified Indicators
Note: Tables 1 and 2 refer to Tables 1 and 2 in the original specification, which are respectively the Simulation Sensor Interface Requirements and Multimodal Interaction Interface Requirements tables. Due to the extensive content, they are not listed here; the original specification or official release documents can be viewed in the report library.
Data Source: Specification for Simulation Test Platforms for Humanoid Robots, compiled by MIR DATABANK
Block 4: Test Methods and Appendix A, i.e., the "Examination Process"
Each indicator is accompanied by an operable detection process:
Test Computer Configuration: CPU ≥ 8 cores/16 threads, GPU VRAM ≥ 16GB, RAM ≥ 64GB, SSD ≥ 500GB;
How to Test Physical Consistency: Requires building a real laboratory with a 3m×3m optical motion capture system, a six-axis force plate, and a 1000Hz high-speed camera, using collision data from real aluminum blocks and a real robot forearm hitting a table to compare with simulation results;
Recommended Exam Paper (Appendix A, informative): The standard provides recommended standard test scenarios, including a 10m flat area, a 15° slope, rough road surfaces, and6-step stairs, along with reference parameters such as friction coefficient and light intensity. It serves as a "recommended unified benchmark" facilitating horizontal comparison of test results across vendors, but as an informative appendix, it is not mandatory.
Given this, the testing cost is not low. Physical consistency testing requires professional laboratories and alignment with real experiments, meaning small teams will likely need to rely on third-party testing institutions.
Part III: Who Does This Standard Impact?
Table 6: Scope of Standard Impact
Data Source: Compiled by MIR DATABANK
GB/T 48316—2026 does not specify concrete scenarios such as factory handling, warehouse sorting, commercial retail, or home services. It enables buyers to more easily require suppliers to disclose simulation conditions, random seeds, scene configurations, log fields, and replay consistency. Regarding whole-machine metrics such as grasping success rate, task completion time, and disturbance recovery, while there are already benchmarks in public reports, formal determination still awaits the full text of the Technical Requirements for Humanoid Robots, accompanying test methods, and certification rules.
Conclusion
GB/T 48316—2026 is the first national unified specification for simulation test platforms in the humanoid robot field. It governs the quality of the "data factory" and the "examination venue." Only when raw materials are credible, rulers are unified, and processes are replayable does the industry have a foundation for scaled development.
Standard Query Method: The full text of GB/T 48316—2026 Specification for Simulation Test Platforms for Humanoid Robots can be searched and viewed on the National Standards Information Public Service Platform (std.samr.gov.cn); the numbers, names, release, and implementation dates of the six standards can be verified through the National Standard Announcement Query System (Announcement No. 43 of 2026).