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The rapid development of humanoid robots has captured significant attention, with their potential to revolutionize industries across the board. However, when it comes to practical, real-world applications, these robots still face substantial challenges. Most notably, they are often still demonstrated and operated remotely, lacking the advanced autonomy necessary to operate independently in dynamic environments. The gap between current capabilities and the levels of autonomy required for humanoid robots to perform efficiently in everyday settings is still measured in years, not months
In the meantime, utility robots—those designed for specific, often repetitive tasks such as reception, cleaning, and logistics—are already making an impact in businesses and industries. By equipping these robots with advanced AI and spatial computing capabilities, we can significantly improve their functionality, efficiency, and ability to handle complex tasks with minimal human intervention.
While humanoid robots grab the spotlight, utility robots have a practical and immediate value to paying customers. These robots are already deployed in environments where automation can bring measurable benefits. From guiding visitors in a reception area to automating the repetitive tasks of floor cleaning or material transport in warehouses, utility robots are increasingly becoming indispensable in these environments. They are not only reducing the need for human labor in dangerous or monotonous tasks but also enabling businesses to streamline operations.
The challenge, however, lies in their current limitations. Many of these robots still rely on basic pre-programmed instructions and lack the advanced AI needed to adapt to dynamic situations. This is where spatial computing and AI integration come into play. By incorporating these technologies, we can allow utility robots to make real-time decisions, map their environments with greater precision, and learn from experience—paving the way for true autonomy.
Spatial computing allows robots to navigate and understand the space around them. With real-time mapping and sophisticated sensor fusion, robots can create dynamic models of their environments, which can be used to avoid obstacles, optimize paths, and understand contextual information in a more meaningful way. Coupled with AI, these robots can not only understand their environment but also learn and adapt, making them far more effective in unpredictable situations.
For example, an AI-powered cleaning robot could identify areas of a room that require more attention, adjust its cleaning schedule based on usage patterns, and even predict maintenance needs before they become issues. Similarly, reception robots with spatial computing and AI could offer personalized experiences, adjusting to the specific needs of different users or detecting when a more human interaction might be required.
Humanoid robots will undoubtedly play a significant role in the future of robotics. However, with the current state of technology, it makes more sense to focus our efforts on enhancing the capabilities of existing utility robots with AI and spatial computing. These technologies can help address real-world challenges today, providing immediate value and making businesses more efficient in the process.
By advancing the capabilities of robots already in use, we can bridge the gap between current technology and the more advanced systems we envision for the future. This focus will allow us to build a foundation that can support the next generation of robots—whether humanoid or otherwise—when the time is right.
The posemesh is a decentralized machine perception network and collaborative spatial computing protocol designed to enhance AI and spatial computing capabilities in robots. By providing a robust solution for accurate pose estimation, the posemesh enables robots to better understand and interact with their environments in real-time. With the posemesh, robots can map their surroundings with greater precision and make more informed decisions as they navigate complex spaces. This capability is particularly beneficial for tasks like material handling, reception, and cleaning, where spatial awareness and adaptability are crucial.
Additionally, the posemesh addresses the challenge of interoperability between robots from different manufacturers. It supports the seamless sharing of information across various robotic systems, enabling collaboration and data exchange that improves overall efficiency. This ability to create a unified network of robots—despite differing hardware and software—is a game-changer for industries relying on multiple types of robots working together in shared spaces. By utilising the posemesh, we can enhance the performance and efficiency of utility robots, making them smarter and more autonomous—ready to tackle today’s challenges and build a stronger foundation for the future of robotics.
Auki is building the Auki network, a decentralized machine perception network for the next 100 billion people, devices and AI on Earth and beyond. The Auki network is a posemesh, an external and collaborative sense of space that machines and AI can use to understand the physical world.
Our mission is to improve civilization’s intercognitive capacity; our ability to think, experience and solve problems together with each other and AI. The greatest way to extend human reach is to collaborate with others. We are building consciousness-expanding technology to reduce the friction of communication and bridge minds.
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The Auki network is a posemesh: a decentralized machine perception network and collaborative spatial computing protocol, designed to allow digital devices to securely and privately exchange spatial data and computing power to form a shared understanding of the physical world.
The Auki network is an open-source protocol that powers a decentralized, blockchain-based spatial computing network. Designed for a future where spatial computing is both collaborative and privacy-preserving, it limits any organization's surveillance capabilities and encourages sovereign ownership of private maps of personal and public spaces.
The decentralization also offers a competitive advantage, especially in shared spatial computing sessions, AR for example, where low latency is crucial.
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