Shenzhen Unveils Action Plan for Embodied AI Robot Technology Innovation and Industry Development, Targeting Industry Scale Exceeding 100 Billion Yuan

2026 is destined to be a pivotal year for embodied intelligence, transitioning from "concept heat" to "real-world application." From human-robot dance on the Spring Festival Gala stage to collaborative operations in factory workshops, and to heartwarming companionship in home settings, humanoid robots and embodied intelligence systems are penetrating various industries at an unprecedented pace. However, as robots' "iron feet" step into the complex soil of the real world, an unavoidable question emerges: who pays when equipment falls? Who is liable for algorithmic errors? Who compensates for data breaches?

In March this year, the Ministry of Science and Technology, the National Financial Regulatory Administration, and other four departments jointly issued a document explicitly encouraging the development of specialized science and technology insurance products in key fields such as embodied intelligence. In June, the Ministry of Industry and Information Technology and the State-owned Assets Supervision and Administration Commission launched a special action requiring the normalized deployment of over 100 high-value application scenarios within the year. The policy tailwind has arrived, but whether the insurance supply side can keep up, adapt, and expand remains an urgent and practical issue.

From "Physical Damage" to "Algorithm Failure": Insurance Vision Needs Broadening

Traditional industrial robots are confined within safety fences, repeating fixed actions, with risks essentially being "mechanical failure + operational error." In contrast, embodied intelligence robots possess autonomous perception, real-time decision-making, and environmental adaptability, causing risk dimensions to expand exponentially.

The first layer is physical damage risk. Robots may collide, fall, or accidentally touch objects during racing, mall guidance, or home services, causing significant damage to themselves. According to industry insiders, a single fall requiring motor replacement can cost 20,000 yuan, and high-end models may incur repair costs exceeding one million yuan—potentially also injuring bystanders or damaging surrounding facilities, leading to third-party personal injury and property claims.

The second layer is technology and algorithm risk. This is the most significant risk distinguishing embodied intelligence from traditional equipment. The "black box" nature of deep learning models makes it difficult to trace accident causes through traditional fault tree analysis. When a robot makes a dangerous action due to perception module misjudgment, is the responsibility attributed to algorithm training data, software code, or hardware sensors? Existing insurance clauses lack targeted provisions for such scenarios.

The third layer is cybersecurity and data privacy risk. Embodied intelligence robots are "walking IoT terminals." If maliciously controlled, the consequences become directly physical. Meanwhile, home companion robots collect vast amounts of emotional, behavioral, and biometric data, posing significant leakage risks.

The fourth layer is ambiguous liability determination risk. In human-robot collaborative operations, accidents may result from a combination of hardware defects, software vulnerabilities, AI decision biases, user misuse, or even environmental interference. Traditional liability insurance assumes a "single at-fault party," which often proves inadequate in multi-causal scenarios.

Different Types of Entities Require Differentiated Protection

During research, the author found that enterprises at different positions in the industry chain have vastly different risk pain points and insurance demands. A simple "one-size-fits-all" product is bound to be unsuitable.

Complete machine manufacturers: High product value, rapid iteration, and frequent commercial performances and rentals. Their most urgent needs are "rapid body repair coverage" and "third-party liability insurance in public places." Due to uncertain project timelines, they require short-term products with flexible daily or weekly coverage. Additionally, post-delivery quality assurance liability and R&D interruption losses are uncovered blind spots.

Technology R&D and scenario application enterprises: Technology routes are not yet converged, algorithms update weekly, and risks are in dynamic drift. In industrial human-robot collaboration scenarios, robots share space with workers, and no unified industry safety standards exist. The biggest challenge for these enterprises is "unquantifiable risk"—lack of historical data makes it difficult for insurers to price, leading to either rejection or exorbitant premiums.

Home consumer-oriented enterprises: Directly facing C-end elderly, children, and other groups, with extremely high human-robot interaction frequency, making them most sensitive to personal injury risks from product defects. Meanwhile, user privacy data protection has become a focus of regulatory and public scrutiny. These enterprises hope insurance can be embedded into products as a "safety endorsement" for consumer trust, rather than merely an after-the-fact compensation tool.

Scenario operators: Displaying and operating high-value robots in public places such as 4S stores face multiple risks including exhibit damage, accidental audience contact, and operational demonstration errors. Their needs are closer to "comprehensive event liability insurance," and they tend to prefer bundled procurement of equipment leasing and maintenance services.

Four Common Challenges Urgently Need Solutions

Comparing enterprise feedback with existing product supply, the current embodied intelligence insurance market faces four structural contradictions.

First, data vacuum leads to pricing failure. Humanoid robots have been commercialized for a short time, with almost no claims data globally. Currently, industry pricing often references drones or industrial robots, but their failure modes and loss distributions differ significantly from embodied intelligence, resulting in large errors.

Second, liability boundaries exceed traditional clause frameworks. Most non-auto insurance products cover "losses caused by external factors," while damage caused by "internal factors" such as hardware aging, software bugs, or AI misjudgment is often excluded—yet these are the most common types of losses enterprises encounter.

Third, the contradiction between fragmented scenarios and product standardization. From factories to homes, from competitions to rentals, each scenario's risk frequency, severity, and liable parties vary greatly. Existing products are mostly "comprehensive" annual packages, lacking flexible, modular designs, making them unaffordable for small and medium-sized enterprises. Among these, pilot testing and scenario adaptation risks are often overlooked but have high accident rates. When robots move from laboratories to real factories, malls, or homes, factors such as ambient lighting, floor materials, and electromagnetic interference vary widely, leading to frequent software-hardware incompatibilities and perception failures during pilot testing.

Fourth, market awareness is severely lagging. Many complete machine enterprises and end users are unaware of the existence of relevant insurance products, making risk retention the default choice. Once a major accident occurs, it will undermine public trust in the entire industry.

From "Passive Underwriting" to "Active Risk Control + Ecosystem Co-building"

Facing these challenges, the insurance industry cannot afford to wait for data accumulation but should take proactive measures, empowering risk control with technology and adapting products to scenarios through innovative models.

First, build an industry-level data sharing infrastructure. It is recommended that regulators or industry associations take the lead in establishing an embodied intelligence safety operation database, collecting anonymized robot operation logs, fault records, environmental parameters, and other data. Insurers can develop "algorithmic pricing models" based on this, using simulation and digital twin technologies to compensate for insufficient historical data. Simultaneously, encourage insurance companies and technology enterprises to establish "data cooperation mechanisms" for dynamic risk assessment under compliance.

Second, implement a "modular + scenario-based" product matrix. Abandon the "one policy covers all" approach, breaking coverage into independent modules such as "body damage," "third-party liability," "product liability," "cybersecurity," "business interruption," and "R&D expense loss," allowing enterprises to combine and insure flexibly according to scenarios, supporting billing by day or project cycle, truly achieving "one scenario, one solution."

Third, explore a new "insurance + service" risk reduction model. Insurers should not only provide "post-loss compensation" but also engage in pre-loss prevention and during-loss intervention. For example, offering value-added services such as robot safety operation assessments, operator training, and emergency drills to insured enterprises free of charge; collaborating with manufacturers to establish rapid inspection and repair mechanisms, and equipping professional technical appraisal teams. By reducing accident probability, a win-win-win situation of "reasonable premiums, controllable claims, and customer benefits" can be achieved.

Fourth, establish fast-track claims channels and legal support mechanisms. Embodied intelligence accident assessment involves software-hardware collaborative diagnosis and is highly specialized. It is recommended that insurance companies sign cooperation agreements with main engine manufacturers, with claims specialists connecting within 30 minutes after an incident, and manufacturer technicians assisting remotely or on-site to determine the cause and extent of loss, committing to complete compensation within 7 working days. Additionally, form a legal advisory team to assist in liability determination and dispute resolution, alleviating enterprises' concerns.

The embodied intelligence industry should join hands with industry, academia, and regulators to accelerate the formulation of industry standards and model clauses for embodied intelligence insurance, encouraging more market participants to engage in innovative pilots. When Chinese humanoid robots step onto the world stage, the accompanying technology insurance solutions can also become globally leading "Chinese solutions."