Robotics Platform
The ABCD stack applied to physical AI. Each layer enables autonomous machines.
The Stack
| Layer | Function | Robotics Application | Key Players |
|---|---|---|---|
| A - AI | Pattern recognition, planning | Navigation, manipulation, task learning | Foundation models, robotics-specific AI |
| B - Blockchain | Immutable record | Proof of work, ownership, maintenance history | Solana, Ethereum, purpose-built |
| C - Crypto | Aligned incentives | Token rewards for task completion, staking for quality | Protocol-specific tokens |
| D - DePIN | Physical layer | The robot itself — distributed fleet ownership | Tesla, Unitree, community fleets |
The thesis: Own the robots → own the task data → own the predictions → own the workflow.
Layer D: Physical Robots
The hardware layer. The robot itself — chassis, actuators, sensors, onboard compute.
Form Factors
| Form | Use Case | Example | Autonomy Level |
|---|---|---|---|
| Humanoid | General labor | Tesla Optimus, Figure 02 | High — full manipulation |
| Quadruped | Inspection, patrol | Unitree Go2, Boston Dynamics Spot | Medium — locomotion + sensing |
| Drone | Mapping, delivery | DJI, Zipline, Spexi | Medium — aerial mobility |
| Wheeled | Delivery, agriculture | Sheep Robotics, Starship | Medium — ground mobility |
| Arm | Manufacturing, surgery | Franka, Universal Robots | High — precision manipulation |
DePIN Fleet Model
Traditional: Company → Buys fleet → Operates → Captures all value
DePIN: Protocol → Operators buy units → Earn from tasks → Community captures value
The shift: Ownership distributes. Operators fund individual robots. Protocol coordinates the fleet. Revenue distributes to operators proportional to task completion.
Layer C: Token Economics
How crypto aligns the robotics ecosystem.
Token Mechanisms
| Mechanism | Purpose | Robotics Application |
|---|---|---|
| Task rewards | Incentivize work | Tokens per completed task |
| Quality staking | Ensure reliability | Stake slashed for task failure |
| Data rewards | Incentivize learning | Tokens for training data contribution |
| Governance | Protocol decisions | Token-weighted fleet parameters |
Token Flow
Task Buyer → pays tokens → Protocol → distributes to:
├── Robot operator (70-80%)
├── Data contributors (10-15%)
└── Protocol treasury (5-10%)
Layer B: Blockchain Infrastructure
Coordination and settlement for autonomous machines.
Functions
| Function | What It Enables | Why Blockchain |
|---|---|---|
| Task settlement | Instant payment on completion | No invoicing delay |
| Proof of work | Verified task execution | Trustless attestation |
| Ownership registry | Robot and fleet ownership | Transparent, transferable |
| Maintenance log | Service history | Immutable record |
| Reputation | Operator quality score | On-chain, portable |
Machine Identity
Every robot needs a self-sovereign identity on-chain. This enables:
- Task assignment based on capability
- Reputation accumulation across tasks
- Ownership transfer and fleet management
- Maintenance and warranty tracking
Layer A: AI and Intelligence
The capability layer. Converts sensor data into decisions and actions.
AI Functions in Robotics
| Function | What It Does | Data Source |
|---|---|---|
| Navigation | Plan and execute movement | Maps, LIDAR, cameras |
| Manipulation | Grasp and move objects | Force sensors, cameras |
| Perception | Understand environment | Multi-modal sensor fusion |
| Planning | Sequence complex tasks | Task specifications, world models |
| Learning | Improve from experience | Fleet task data |
The Learning Loop
Sensor Data → Perception → Decision → Action → Outcome → Training Update
↑ ↓
└──────────── Updated model improves next cycle ────────┘
Fleet-scale learning: When 1,000 robots encounter 1,000 different situations, the combined learning exceeds any single-robot training run. This is the network effect in physical AI.
Model Architecture
| Layer | Model Type | Function |
|---|---|---|
| Foundation | Large multimodal models | General reasoning and planning |
| Domain | Robotics-specific models | Movement, manipulation, navigation |
| Task | Fine-tuned models | Specific task execution |
| Safety | Constraint models | Collision avoidance, human safety |
Stack Integration
| Missing Layer | What Breaks | Result |
|---|---|---|
| No DePIN | No physical robots | Nothing to coordinate |
| No Crypto | No incentive alignment | No one deploys robots |
| No Blockchain | No trust in autonomous work | Requires human oversight |
| No AI | No autonomous capability | Expensive teleoperation |
Full Stack Flow
D (Robot executes) → B (Chain records proof) → C (Token rewards operator) → A (AI learns from data)
↑ ↓
└──────────── A (Better AI) enables D (harder tasks) ───────────────────┘
Data Dependencies
Robotics depends on other DePIN data infrastructure:
| Data Need | Source | Provider |
|---|---|---|
| Positioning | RTK corrections | GEODNET |
| Maps | Street-level imagery | Hivemapper |
| Connectivity | Network coverage | Helium |
| Compute | GPU for training | io.net, Render |
| Weather | Environmental data | WeatherXM |
Context
- Robotics Overview — The transformation thesis
- Protocols — Three Flows and Intercognitive
- Players — Who builds at each layer
- AI Data Platform — Data stack that feeds robots
- ABCD Stack — The broader technology framework