Overview
What this challenge is about.
Design reward variants for a quadruped locomotion policy, train PPO, and recommend the best one. Earn a verifiable certificate.
The scenario
The startup (around 60 staff, Series A, around 200 robots in the field) is preparing to ship the new actuator across the fleet next quarter; the locomotion policy is the gating dependency.
The Brief
What you'll do, and what you'll demonstrate.
Rework the locomotion reward function to handle higher torque noise without sacrificing forward velocity or stability.
Earning criteria — what you'll demonstrate
- Apply principled reward shaping to a deep-RL locomotion task
- Use curriculum-style reward annealing for stability
- Evaluate locomotion policies on multiple operational metrics, not just success
- Communicate reward-engineering choices to an applied robotics 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.
- Reward Shaping
Apply reward shaping to solve real industry problems and demonstrate production-level capability.
- Ppo
Apply ppo to solve real industry problems and demonstrate production-level capability.
- Locomotion
Apply locomotion to solve real industry problems and demonstrate production-level capability.
- Curriculum Learning
Apply curriculum learning 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.
- Policy Evaluation
Apply policy evaluation 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:
Machine Learning Engineer
Reward-shaping for a real hardware constraint is the kind of MLE judgement work that ships robotics products.
This challenge sharpens
- reward-shaping
- ppo
- deep-rl
ML Researcher
Structured ablation across reward formulations is research-engineering work that opens doors at robot-learning labs.
This challenge sharpens
- reward-shaping
- curriculum-learning
- policy-evaluation
Research Scientist
Multi-metric locomotion evaluation with seed variance is the rigor research-scientist roles need.
This challenge sharpens
- policy-evaluation
- deep-rl
- locomotion