18 Transfer Learning & Sim-to-Real
What knowledge remains true when the world changes?
Area 18: Transfer Learning & Sim-to-Real
Master Question
What knowledge remains true when the world changes?
What We Want to Discover
A policy trained in one simulator, on one task, or with one robot body does not automatically work somewhere else. This area studies which parts of a learned policy or model actually depend on the specifics of the training setting, which parts are more general, and what techniques narrow the gap when a system is deployed outside its original training distribution.
Why It Matters
Simulation is cheap and safe, but the real world is where systems must ultimately operate. Without a principled account of what transfers and what does not, sim to real deployment remains a matter of trial and error rather than engineering.
Core Concepts
- Domain randomization
- Domain adaptation
- Reality gap
- Transfer gap measurement
- Fine tuning versus zero shot transfer
Relevant Disciplines
- Statistics, particularly distribution shift
- Cognitive science of analogical reasoning
- Control theory
Potential Mimicry Sources
- Human skill transfer between related tasks
- Animal generalization from training to natural conditions
- Cross training in athletics
Projects
No projects yet.
Active Questions
No projects yet, so there are no active derived questions to report.
Key Findings Across Projects
Pending. No projects in this area have produced findings yet.
Unresolved Questions
Pending.
Connections to Other Areas
Pending. Connections will be identified as projects in this area develop.