← AI Terminology
Sim-to-Real
Sim-to-real is the transfer of policies or models trained in simulation to physical robots or real environments, bridging the reality gap.
A central challenge in robot learning.
A central challenge in robot learning.
Why It Matters in AI
Real robot data is slow and risky; sim is plentiful but biased. Domain randomisation, system identification, and real fine-tuning enable deployable skills — essential for modern robotics AI.
Key Points
| Aspect | Description |
|---|---|
| Gap | Physics, sensors, latency differ from sim |
| Metric | Real-world success rate after transfer |
| Stacks | Isaac Sim, MuJoCo, Habitat + real robots |
| Methods | Domain randomisation, domain adaptation, real residual learning |
| Related | Model-based RL, imitation, embodied AI |
| Practice | Start simple skills; instrument carefully |
Simple Analogy
Flight sim hours before piloting a real plane — useful practice, but wind and metal still feel different on the first live takeoff.
Common Usage Examples
- Domain-randomise textures/masses in sim
- Deploy locomotion policies on hardware
- Few real demos to adapt
- Measure gap ablations
Summary
In short: Sim-to-real transfers simulated training to physical systems — closing the reality gap for robot learning.