Integrated field curriculum
Learn the stack, collect real robot data, then read the industry map.
This path connects three reader jobs: learning the robotics stack, understanding robot data systems, and reading the company landscape. It is designed for software engineers, robotics students, and operators who need a usable route from concepts to field work.
Start From Your Job
| If you are… | Start with | Then continue to |
|---|---|---|
| A software engineer entering robotics | Phase 1 and Phase 2 | Phase 4 for robot learning, then Phase 6 for data systems |
| A robotics student building a project | Phase 2 and Phase 3 | Phase 5 for simulation, then Phase 6 for collection setup |
| A founder or operator evaluating the field | Phase 6 and Phase 7 | Industry map, regions, and Field Notes |
| A researcher comparing model directions | Phase 4 and Phase 5 | VLA, diffusion policy, sim-to-real, and data quality guides |
The Integrated Route
| Layer | Source backbone | Reader job | Output |
|---|---|---|---|
| Course map | Structured curriculum | Build the conceptual foundation | 18-lesson sequence from foundations to sim-to-real |
| Practice layer | Data-collection playbooks | Understand robot data systems | Teleoperation, tactile sensing, data quality, data-center patterns |
| Industry layer | Company map | Know who is building what | Company map by region, supply-chain layer, and category |
This is not a bulk import of old pages. The goal is to turn scattered notes into a field guide where each page answers a concrete reader question.
Phase 1: Foundations
Reader question: What is embodied AI, and how is it different from conventional AI?
Course units:
- What is embodied AI?
- Embodied AI vs traditional AI
- Core technology stack overview
- Development environment setup
Read first:
Checkpoint: You can explain why robot intelligence needs sensors, actions, feedback, and environment interaction instead of only text/image prediction.
Phase 2: Perception
Reader question: What must a robot perceive before it can act?
Course units:
- Visual perception basics
- Depth perception and 3D vision
- Tactile and force sensing
- Multimodal perception fusion
Read next:
Practice connection: data-collection setups increasingly combine RGB-D cameras, wrist cameras, force/torque signals, tactile sensors, and operator commands. Treat the sensor stack as part of the dataset, not only part of the robot.
Checkpoint: You can describe what each modality contributes and why tactile or force data matters for contact-rich manipulation.
Phase 3: Motion And Control
Reader question: How does a policy become motion on hardware?
Course units:
- Robot kinematics
- Dynamics and force control
- Trajectory planning
- Motion control practice
Read next:
Industry connection: robot-body companies and upstream actuator suppliers are not interchangeable. A humanoid company, a joint-module company, and a sensor company create different constraints for control latency, payload, data fields, and deployment.
Checkpoint: You can separate model inference from controller execution, and you can explain why robot hardware changes the shape of the learning problem.
Phase 4: Robot Learning
Reader question: Which learning method fits the task?
Course units:
- Reinforcement learning basics
- Imitation learning
- Vision-language models
- End-to-end policy learning
Read next:
Practice connection: most serious current-stage robot learning pipelines still need high-quality demonstrations. The practical question is not “RL or IL” in isolation; it is how simulation, teleoperation, human correction, and real deployment data are combined.
Checkpoint: You can choose between imitation learning, reinforcement learning, VLA-style policies, and diffusion policies for a concrete task.
Phase 5: Simulation And Sim-to-Real
Reader question: How do simulated skills survive contact with the real world?
Course units:
- Simulation platform practice
- Sim-to-real transfer
Read next:
Checkpoint: You can explain the visual gap, physics gap, dynamics gap, and why real data remains necessary even when simulation is useful.
Phase 6: Data Collection Systems
Reader question: What does a real robot data pipeline look like?
The practical layer should be read after the learning foundations because it turns the curriculum into operator decisions.
| Scenario | Typical setup | What to study |
|---|---|---|
| Desktop manipulation | Leader-follower arms, wrist cameras, RGB-D, grippers | ALOHA-style datasets, LeRobot, reset protocols |
| Mobile manipulation | Mobile base, arm, onboard compute, remote operator | latency, localization, safety stop, operator workflow |
| Force/tactile collection | Dexterous hand, force/torque, tactile skin | contact labels, slip detection, failure recovery |
| Data center / data factory | Multiple rigs, trained operators, QA workflow | dataset versioning, operator bias, throughput, cost control |
Read next:
Checkpoint: You can draft a small dataset plan: task definition, hardware stack, data schema, demo count, QA method, and retraining loop.
Phase 7: Industry Map
Reader question: Who is building the stack, and where do they sit in the value chain?
The company map turns the learning path into market orientation. Use it after you understand the stack, not before.
| Lens | Why it matters |
|---|---|
| Region | Robotics clusters differ by supply chain, universities, manufacturing base, and capital access |
| Chain position | Upstream sensors/actuators, midstream robot bodies, downstream applications, algorithm layers have different bottlenecks |
| Category | Humanoid bodies, industrial robots, service robots, actuators, sensors, and algorithms should not be evaluated with the same criteria |
Read next:
Checkpoint: You can look at a company and ask the right first question: is this a model company, robot-body company, component supplier, application operator, or data infrastructure company?
What Comes After This Path
| If you want to… | Continue with… |
|---|---|
| Build a robot learning prototype | LeRobot, data collection methods, sim-to-real transfer |
| Track companies and supply chain | Industry map, China companies, funding tracker |
| Follow weekly changes | Field Notes and RSS |
| Validate article reliability | Editorial Policy and source notes inside articles |
Source Note
This page consolidates course, practice, and industry material into a reader-facing route. Company and product claims should be treated as a starting map and verified against primary sources before investment, procurement, or research citation.