With the amount of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time
With the amount of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time
Reboot Subnet (SN47) is a specialized decentralized robotics AI network built on the Bittensor blockchain. It is designed as a competitive, incentive-driven framework: Miners contribute robotics algorithms, control models, or synthetic data pipelines, while Validators evaluate those contributions for correctness, safety, and efficiency. Together they create a self-improving loop where useful robotics solutions are rewarded. Reboot provides a flexible framework for robotics AI, enabling tasks such as:
Robot Control: AI-driven decision-making and motion control for autonomous agents.
Sensor Data Processing: Distributed analysis and fusion of multi-modal sensor streams (cameras, LiDAR, IMU, etc.).
Task Execution: Coordinated multi-robot task allocation and execution planning.
Learning & Adaptation: Continuous improvement of models from shared robot experience data.
Safety & Validation: Automated AI-driven safety checks and operational verifications for robotic actions.
Synthetic Data Generation: Creation of simulated robotics datasets (via contests and simulators) to train and test algorithms.
Miners on Reboot deliver these robotics AI services, while Validators score and verify them to ensure accuracy and safety. This architecture makes Reboot both a testbed for new robotics algorithms and a provider of synthetic data for next-generation robotic AI.
Reboot Subnet (SN47) is a specialized decentralized robotics AI network built on the Bittensor blockchain. It is designed as a competitive, incentive-driven framework: Miners contribute robotics algorithms, control models, or synthetic data pipelines, while Validators evaluate those contributions for correctness, safety, and efficiency. Together they create a self-improving loop where useful robotics solutions are rewarded. Reboot provides a flexible framework for robotics AI, enabling tasks such as:
Robot Control: AI-driven decision-making and motion control for autonomous agents.
Sensor Data Processing: Distributed analysis and fusion of multi-modal sensor streams (cameras, LiDAR, IMU, etc.).
Task Execution: Coordinated multi-robot task allocation and execution planning.
Learning & Adaptation: Continuous improvement of models from shared robot experience data.
Safety & Validation: Automated AI-driven safety checks and operational verifications for robotic actions.
Synthetic Data Generation: Creation of simulated robotics datasets (via contests and simulators) to train and test algorithms.
Miners on Reboot deliver these robotics AI services, while Validators score and verify them to ensure accuracy and safety. This architecture makes Reboot both a testbed for new robotics algorithms and a provider of synthetic data for next-generation robotic AI.
Reboot’s “product” is an open-source Python software stack implementing a Bittensor subnet tailored for robotics AI. It runs on Python 3.11+ (using the Bittensor framework) with optional Docker container support for reproducible deployments. The GitHub repository (MIT-licensed) contains the core subnet logic, including robotics controllers, neuron modules, simulation tools, and deployment scripts. It is designed to integrate with standard robotics simulators and environments, so developers can test algorithms in virtual settings and iterate rapidly. Key features of the product include:
Core Framework: Specialized AI models for robotic control and planning, multi-sensor data fusion, low-latency distributed planning, and an AI-driven safety validation layer.
Deployment: Fully Dockerized and container-ready for consistent environments.
Integration: Built-in support for multi-modal perception (cameras, LiDAR, IMU) and easy connection to robotics simulation environments for synthetic data generation.
Modularity: Extensible architecture with clean base classes, custom protocol plugins, flexible configuration files, and comprehensive unit/integration tests for reliability.
Under the hood, Reboot leverages Bittensor’s blockchain consensus and token (TAO) incentives. In practice, miners stake TAO to join SN47 and earn rewards by providing useful robotics AI, while validators earn by fairly scoring outputs. The project also offers command-line scripts and a sample “min_compute” YAML setup for running nodes on minimal hardware. Overall, Reboot is both a research testbed and a production-ready framework: developers worldwide can plug in new models or data, and deploy them on the network under the existing incentive scheme.
Reboot’s “product” is an open-source Python software stack implementing a Bittensor subnet tailored for robotics AI. It runs on Python 3.11+ (using the Bittensor framework) with optional Docker container support for reproducible deployments. The GitHub repository (MIT-licensed) contains the core subnet logic, including robotics controllers, neuron modules, simulation tools, and deployment scripts. It is designed to integrate with standard robotics simulators and environments, so developers can test algorithms in virtual settings and iterate rapidly. Key features of the product include:
Core Framework: Specialized AI models for robotic control and planning, multi-sensor data fusion, low-latency distributed planning, and an AI-driven safety validation layer.
Deployment: Fully Dockerized and container-ready for consistent environments.
Integration: Built-in support for multi-modal perception (cameras, LiDAR, IMU) and easy connection to robotics simulation environments for synthetic data generation.
Modularity: Extensible architecture with clean base classes, custom protocol plugins, flexible configuration files, and comprehensive unit/integration tests for reliability.
Under the hood, Reboot leverages Bittensor’s blockchain consensus and token (TAO) incentives. In practice, miners stake TAO to join SN47 and earn rewards by providing useful robotics AI, while validators earn by fairly scoring outputs. The project also offers command-line scripts and a sample “min_compute” YAML setup for running nodes on minimal hardware. Overall, Reboot is both a research testbed and a production-ready framework: developers worldwide can plug in new models or data, and deploy them on the network under the existing incentive scheme.
Reboot is developed and maintained by an open community of AI and robotics researchers. There is no single public “founder” listed – instead, contributions come from global developers via GitHub and community channels. The official documentation notes that Reboot is “built and sustained by an open community of developers, researchers, and robotics enthusiasts.”. (The Reboot GitHub organization has no public member list.) Contributors collaborate through the project’s GitHub repo and a public Discord server for support and coordination.
Reboot is developed and maintained by an open community of AI and robotics researchers. There is no single public “founder” listed – instead, contributions come from global developers via GitHub and community channels. The official documentation notes that Reboot is “built and sustained by an open community of developers, researchers, and robotics enthusiasts.”. (The Reboot GitHub organization has no public member list.) Contributors collaborate through the project’s GitHub repo and a public Discord server for support and coordination.
Development proceeds in four phases:
Phase 1 – Launch (Now): Core subnet is live on Bittensor, with initial competitive robotics tasks (e.g. pathfinding, planning) and a real-time validator scoring leaderboard.
Phase 2 – Expansion: Introduce multi-agent coordination challenges, create simulated humanoid motion and embodied AI datasets, and integrate more deeply with robotics simulators.
Phase 3 – Ecosystem Growth: Build a synthetic data marketplace with open access, encourage community-contributed/curated datasets worldwide, and collaborate with other Bittensor subnets on joint use cases.
Phase 4 – Long-Term Vision: Establish Reboot as the global backbone for synthetic robotics data – by then, robots trained on Reboot-generated datasets should deploy faster and safer in the real world.
Each phase builds on the last, evolving Reboot from a proof-of-concept into a comprehensive, decentralized robotics AI ecosystem.
Development proceeds in four phases:
Phase 1 – Launch (Now): Core subnet is live on Bittensor, with initial competitive robotics tasks (e.g. pathfinding, planning) and a real-time validator scoring leaderboard.
Phase 2 – Expansion: Introduce multi-agent coordination challenges, create simulated humanoid motion and embodied AI datasets, and integrate more deeply with robotics simulators.
Phase 3 – Ecosystem Growth: Build a synthetic data marketplace with open access, encourage community-contributed/curated datasets worldwide, and collaborate with other Bittensor subnets on joint use cases.
Phase 4 – Long-Term Vision: Establish Reboot as the global backbone for synthetic robotics data – by then, robots trained on Reboot-generated datasets should deploy faster and safer in the real world.
Each phase builds on the last, evolving Reboot from a proof-of-concept into a comprehensive, decentralized robotics AI ecosystem.