Overview
What this challenge is about.
Plan Under Uncertainty for a Warehouse Restocking Robot. Expert-level challenge in code. Writing production code that solves real engineering problems, earn ...
The Brief
What you'll do, and what you'll demonstrate.
Build a POMDP planner that reduces stock-outs in a real warehouse simulator without inflating robot idle time.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Formulate a real-world problem as a POMDP
- Implement and tune a Monte Carlo planner over beliefs
- Compare planners on operations-level KPIs, not just expected reward
- Communicate planner behavior to an operations audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Advanced Robotics
Master · Applied Ai
Strong alignment
This challenge maps to Advanced Robotics at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Planning Under Uncertainty
Apply planning under uncertainty to solve real industry problems and demonstrate production-level capability.
- Pomdp
Apply pomdp to solve real industry problems and demonstrate production-level capability.
- Monte Carlo Planning
Apply monte carlo planning to solve real industry problems and demonstrate production-level capability.
- Simulation
Apply simulation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Operations Research
Apply operations research 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:
AI Engineer
Shipping a planner against real operational KPIs and writing an ops-team rollout plan is the AI-engineer pattern at warehouse-tech companies.
This challenge sharpens
- pomdp
- planning-under-uncertainty
- simulation
ML Researcher
Sensitivity-analysis discipline and faithful problem formulation are the rigor markers ML researchers carry across applied projects.
This challenge sharpens
- monte-carlo-planning
- simulation
- operations-research