The birth of the "App Store" in the robotics world: How far are we from "write once, run on all robots"?

On January 27, 2026, OpenMind announced the launch of its robot app store on the Apple App Store. On the surface, it appears to be just another tech company releasing a new product. However, a closer look reveals that this is the first attempt in the robotics industry to address a more fundamental challenge than “making robots walk”—how to establish a cross-hardware developer ecosystem. When eight companies originally competing—such as UBTech, ZhiYuan Robotics, Fourier, and others—appear together on the partner list, a clear signal emerges: the robotics industry is undergoing a paradigm shift from “hardware competition” to “software ecosystem.” But the real technical challenges are just beginning—how to make a piece of code behave identically on bipedal humanoid robots and quadruped robotic dogs? The answer to this question not only impacts commercial success but will also determine whether robotics technology can integrate into daily life as seamlessly as smartphones.

OM1 Operating System: The “Android Moment” for Robotics or Another Fragmentation Trap?

OpenMind’s open-source OS, OM1, is promoted as the foundation for “cross-entity robots,” but this promise faces nearly contradictory engineering requirements. The diversity of robotic hardware far exceeds that of smartphones—from wheeled chassis to bipedal humanoids, from industrial arms to companion robots—the degrees of freedom, sensor configurations, and mobility capabilities vary greatly. To provide a unified development experience across such diversity, OM1 must make fundamental architectural choices. The hardware abstraction layer’s design philosophy must shift from “device-oriented” to “capability-oriented,” enabling developers to program not specific joints of a robot but abstract motion capabilities. This means the system kernel must maintain a real-time, dynamic list of robot capabilities, intelligently scheduling resources based on actual hardware configurations and environmental conditions.

Designing a secure sandbox becomes another critical challenge. Unlike mobile apps, where crashes typically lead to software restarts, failures in robot applications can cause physical harm. OM1 needs to implement multi-layer security isolation, ensuring third-party applications cannot directly access low-level motor drivers; all motion commands must undergo strict feasibility checks. The system must compute in real-time whether each action stays within the robot’s physical limits, avoids collisions, and complies with energy constraints. An innovative solution might be a “progressive permission” model—newly installed applications initially run in highly restricted simulated environments, gradually gaining more physical control as their reliability is verified.

However, the performance overhead introduced by abstraction layers is an unavoidable issue. Robot control requires millisecond-level real-time responses, and each software abstraction layer adds latency. OM1 appears to adopt a hybrid execution model to address this—critical control loops such as balance maintenance run directly on hardware or in real-time kernels to ensure minimal latency; higher-level application logic executes in user space, interacting with the lower layers through precise priority scheduling and real-time communication mechanisms. This layered architecture must strike an exact balance between flexibility and performance; any deviation could result in a system that is either too rigid to support innovation or too flexible to guarantee real-time responsiveness.

Developers’ New Reality: The Unique Challenges of Coding for the Physical World

Developing applications for robots is fundamentally different from developing for smartphones. In the mobile world, developers can assume a relatively stable computing environment—ample memory, continuous power supply, standard sensors. In the physical world, robot applications must constantly contend with changing constraints: joint torque limits, remaining battery capacity, ground friction coefficients, dynamic obstacles in the environment. OpenMind’s app store requires developers to declare detailed physical requirement lists for each skill, including the number of degrees of freedom needed, sensor types, minimum battery capacity, and whether a stable operating platform is required. The store’s backend matching algorithm intelligently pairs these declarations with each robot’s actual capabilities, preventing applications requiring precise operations from being installed on under-equipped hardware.

The uncertainty of the physical environment presents unique challenges for robot programming. Traditional software runs in deterministic environments—same input always yields same output. But robot applications must handle sensor noise, actuator errors, environmental changes, and other uncertainties. OM1’s software development toolkit offers a set of probabilistic programming primitives, allowing developers to write fault-tolerant code. Instead of issuing absolute commands like “raise arm 30 degrees,” developers describe “try to raise the arm to the target angle; if resistance exceeds a threshold, execute a backup plan.” The system automatically records these uncertainty events and uses them to improve future decision-making strategies. More advanced features include cross-robot knowledge transfer—skills learned on one model can, through proper abstraction and adaptation, be partially transferred to other hardware platforms.

The maturity of the toolchain will determine the quality of the development experience. OpenMind provides a web-based robot simulator, allowing developers to test application logic without physical hardware. But the gap between simulation and reality always exists; no simulation environment can fully reproduce the complexity of the real world. To address this, OpenMind may have established a crowdsourced testing network—developers can submit applications to a distributed testing pool of real robots. These robots come from different manufacturers and operate in various environments, providing diverse testing feedback. Test reports not only help developers improve their applications but also become an important input for app store ranking algorithms, creating a virtuous cycle of quality improvement.

Innovating Business Models: The Technical Realization of the “Skill Economy”

OpenMind’s app store is not just a technological platform but also an economic experiment. When “robot skills” become tradable commodities, a new infrastructure is needed to support digital property rights management, trading, and distribution. Digital rights management in robotics presents unprecedented complexity. Traditional software piracy prevention mainly focuses on code copying, but the essence of robot skills may be sequences of actions or control strategies—how to prevent users from reverse-engineering core algorithms by observing robot behavior? OpenMind’s solution might involve encrypted execution environments, where critical skill code runs in hardware-isolated trusted execution environments, receiving encrypted inputs and outputting control signals without exposing internal logic. Another protection mechanism is hardware binding—certain advanced skills require specific sensor configurations or precision actuation, naturally creating a technical barrier.

Dynamic pricing models require real-time data support. The actual value of a “home cleaning” skill depends on multiple quantifiable metrics: cleaning coverage area, completion time, energy consumption, user satisfaction scores. OpenMind’s backend continuously collects anonymized performance data, running a complex skill efficacy evaluation framework to inform dynamic pricing algorithms. Skill developers can choose various business models, including one-time purchases, subscriptions, or pay-per-use, each requiring different measurement, billing, and verification technologies. Finer models might include tiered pricing—basic features are free to attract users, while advanced features or professional scenarios require paid unlocking.

Skill combination markets could spawn new forms of creativity. Just as mobile app “workflows” can chain multiple tools, robot skills can be combined via standardized interfaces into complex task sequences. A “prepare breakfast” composite skill might include “open refrigerator door,” “identify and grasp eggs,” “safely operate frying pan,” and so on. This requires the system to provide standardized skill interface description languages and composition verification tools, ensuring that combined skills are physically feasible and do not cause conflicting actions. Creating skill combinations might itself become a new creative category—“robot skill architects” skilled at integrating existing skills to create new functionalities could emerge as a profession.