📜Proposition

The solution

Haven offers a decentralized platform where users can run robotic simulations using digital twin models of real-world robots. The DApp allows both technical and natural language inputs to simulate and validate robotic behaviors in physics-based virtual environments—enabling safe, cost-effective experimentation, prototyping, and education without the need for physical ownership. For robot owners, it offers a risk-free testbed; for others, a curiosity-driven playground for learning and innovation.

Task Execution

A pre-set group of tasks is enabled by default for every general bot-type, written in natural language, which acts as simple functions, such as: move_arm, scan_area, move_body_center, grab, navigate_to, resolve, interpretate, random_action — and many more!

Besides these tasks, users will also be able to interact with the machine through a terminal, where they can execute anything from straight code, which, if done correctly, would guarantee the most exact execution due to lack of machine interpretation, up to simple natural language commands, such as "jump", "pick that object", instead of having to 'move_arm' towards the object then 'grab' it, "manually", per-se.

Haven is powering robots with AI understanding of roles & task execution, not just a task pre-set. The solution scope consists of the following 3 key points:

Digital Twin Layer

Every robot has a digital twin (simulation model) that exposes its capabilities (movement, sensors, kinematics). Industry firms can test algorithms safely in simulation before deploying to real robots.

Marketplace & Scheduling

Customers/researchers “rent” robot twin time (simulation cycles). Robot owners can list their twins, stake tokens, and earn yield when used.

Verification & Data Layer

Every experiment produces logs, telemetry, and data outputs.