Overview
What this challenge is about.
Implement an RRT* planner for a sidewalk robot, benchmark it against A*, and report results to earn your verifiable certificate.
The scenario
The startup (around 18 people, one neighborhood pilot in Tel Aviv) has had 4 minor incidents in the last month, each of which would have been avoided by a slightly more conservative path; the ops lead wants quantitative evidence before approving a new planner.
The Brief
What you'll do, and what you'll demonstrate.
Design and benchmark a sampling-based planner that lifts safety clearance without sinking time-to-goal on a real sidewalk dataset.
Earning criteria — what you'll demonstrate
- Implement a sampling-based motion planner with a structured cost
- Design a cost function that encodes social/safety constraints
- Evaluate plans on real-world metrics (clearance, success, time)
- Communicate planner trade-offs to a non-engineering stakeholder
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.
- Motion Planning
Apply motion planning to solve real industry problems and demonstrate production-level capability.
- Sampling Based Planning
Apply sampling based planning to solve real industry problems and demonstrate production-level capability.
- Cost Function Design
Apply cost function design to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Robotics Simulation
Apply robotics simulation 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
Wiring planning algorithms into a real robot stack with measurable safety metrics is everyday AI-engineer work at last-mile robotics companies.
This challenge sharpens
- motion-planning
- python
- evaluation
Machine Learning Engineer
Cost-function design with held-out evaluation is the same discipline MLEs apply to loss functions and policy tuning.
This challenge sharpens
- cost-function-design
- evaluation
- python
Applied AI Scientist
Briefing a non-engineer stakeholder on planner trade-offs is the soft-skill side of applied-AI work in operations-heavy robotics.
This challenge sharpens
- motion-planning
- evaluation
- cost-function-design