Overview
What this challenge is about.
Code a temporal PDDL planner for 30 days of robotics mission logs, then compare performance against the current scheduler. Get a verifiable certificate.
The scenario
The startup (around 40 staff) operates around 25 robots across European museums; missed scheduled events trigger contract penalties at around 4 of every 100 missions.
The Brief
What you'll do, and what you'll demonstrate.
Cut the missed-time-window rate by 50 percent on 30 days of replayed missions with a temporal PDDL planner.
Earning criteria — what you'll demonstrate
- Model durative actions and time windows in PDDL 2.1
- Run a temporal planner on realistic operational data
- Simulate execution variance to stress-test planner robustness
- Translate planner results into a deployment-readiness assessment
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.
- Temporal Planning
Apply temporal planning to solve real industry problems and demonstrate production-level capability.
- Pddl Modeling
Apply pddl modeling to solve real industry problems and demonstrate production-level capability.
- Simulation
Apply simulation to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Constraint Handling
Apply constraint handling 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
Temporal-planner integration on real robot logs is high-leverage AI engineering work at any robotics startup.
This challenge sharpens
- temporal-planning
- pddl-modeling
- constraint-handling
Applied AI Scientist
Replaying real logs with realistic execution variance is the rigorous applied-AI methodology a research-driven robotics company expects.
This challenge sharpens
- simulation
- benchmarking
- temporal-planning
ML Researcher
Treating planner comparison as a controlled experiment with replay data is the ML researcher's contribution to a robotics product team.
This challenge sharpens
- benchmarking
- simulation
- temporal-planning