Overview
What this challenge is about.
Design a factorial study on randomization regimes and training durations for a mobile robot, then evaluate the sim-to-real gap. Earn a verifiable certificate.
The scenario
The startup (around 100 staff, Series B) ships about 30 robots per month and the sim-to-real gap is the single biggest source of post-deploy hot-fixes; closing it is on the engineering OKR.
The Brief
What you'll do, and what you'll demonstrate.
Run a structured domain-randomization study and recommend the regime that minimizes sim-to-real gap for navigation policies.
Earning criteria — what you'll demonstrate
- Design a factorial domain-randomization study under a tight compute budget
- Evaluate sim-to-real gap correctly with a held-out real-bench set
- Reason about visual vs. dynamics randomization trade-offs
- Communicate sim-to-real findings to a robotics engineering team
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Domain Randomization
Apply domain randomization to solve real industry problems and demonstrate production-level capability.
- Sim To Real
Apply sim to real to solve real industry problems and demonstrate production-level capability.
- Robot Navigation
Apply robot navigation to solve real industry problems and demonstrate production-level capability.
- Policy Evaluation
Apply policy evaluation to solve real industry problems and demonstrate production-level capability.
- Deep Rl
Apply deep rl to solve real industry problems and demonstrate production-level capability.
- Experiment Design
Apply experiment design to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
ML Researcher
Designing structured sim-to-real studies is the kind of research-engineering work robot-learning labs and startups hire for.
This challenge sharpens
- domain-randomization
- sim-to-real
- experiment-design
Research Scientist
Properly measuring sim-to-real gap with CIs and held-out benches is the rigor robotics research-scientist roles look for.
This challenge sharpens
- sim-to-real
- policy-evaluation
- experiment-design
Applied AI Scientist
Translating a randomization study into a recommended next training cycle is core applied-AI work at robotics startups.
This challenge sharpens
- domain-randomization
- robot-navigation
- policy-evaluation