> For the complete documentation index, see [llms.txt](https://whitepaper.openvision.network/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://whitepaper.openvision.network/openvision-declaration/the-problem-we-are-solving.md). # The Problem We are Solving OpenVision is addressing the critical challenge within the humanoid robotics industry of acquiring high-quality, diverse, and secure egocentric video training data. The market of robotics training data becomes particularly important with recent surge in demand and market interest for multimodal data 1. **Proof-of-concept and market interests for humanoid robots**: recent collaboration between OpenAI and Figure, Tesla Optimus robot release, Nvidia Project GR00T general-purpose foundation model for humanoid robots, and wide use cases in healthcare and medical, companion, household, manufacturing. Many recent startups such as Physical Intelligence (spin-off from Google Robotics), k-Scale (open-source robotics team from Optimus) is an emerging category that need mass data collection. 2. **Inception of AI and AGI era**: USD 200 billion market expected to grow to over USD 1.8 trillion by 2030; entering pixel-centric AI development, e.g., launch of Sora, emerging VLM 3. **Increasing market adoption of AR glasses**: community hype around Apple VisionPro (\~200,000 headsets sold in 10 Days), New Meta Smart Glasses; Meta’s SceneScript model to enable AI-powered AR glass At its core, OpenVision is a two-sided platform fostering seamless creation and exchange of high-quality egocentric video training data between Data Providers with AI and Robotics companies/ institutions, through a collective data ownership governed by $VISION tokens. Three parties involved are: 1. **AI and Robotics companies/institutions** who are seeking real-life, diverse, and on-demand video-based data for training their models. Ego-centric datasets labelled with actions and skill sets from the human perspective are especially valuable. * Examples: Cowarobot, Figure AI, Boston Dynamics, Optimus (Tesla) 2. **Data Providers** who can pool in real-life data using our proprietary AR hardware. (Later on we will allow users to use their own hardware/wearables and mobile phones to capture data. ) 3. **Data Validators** who assess and mediate and safeguard the integrity of data submitted by reviewing and approving quality submissions. * Examples: AI robotics company, independent AI researchers and enthusiasts --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://whitepaper.openvision.network/openvision-declaration/the-problem-we-are-solving.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.