Overview
What this challenge is about.
You receive 200 human teleoperated demonstrations (state + action trajectories) of picking 8 small electronic components from a tray and placing them at marked locations in a robosuite simulator. Train a behavior cloning (BC) policy with a small MLP on (state, action) pairs. Evaluate on 50 held-out pick scenarios with varied component positions. Score on (a) task success rate per component type, (b) average completion time vs. the scripted baseline, (c) graceful failure rate (drop without crash) on out-of-distribution positions. Success is overall success above 70 percent and time-to-completion within 1.3x of the scripted baseline.
The Brief
What you'll do, and what you'll demonstrate.
Train a behavior cloning policy for pick-and-place that achieves competitive success on held-out scenarios versus a hand-scripted baseline.
Earning criteria — what you'll demonstrate
- Implement behavior cloning from human demonstrations
- Evaluate imitation policies on held-out manipulation scenarios
- Detect and report out-of-distribution behavior
- Communicate imitation-learning trade-offs to a non-ML applications team
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Machine Learning Engineer
Behavior cloning on real demos and shipping a policy with held-out evaluation is the entry-level robotics-MLE job at any robot-arm company.
This challenge sharpens
- behavior-cloning
- imitation-learning
- manipulation
Applied AI Scientist
Evaluating an imitation policy honestly and writing the feasibility memo is core applied-AI-scientist work in industrial robotics.
This challenge sharpens
- policy-evaluation
- imitation-learning
- manipulation
ML Researcher
Multi-seed reporting and per-component breakdowns are the rigor signals research-leaning roles in robot learning look for.
This challenge sharpens
- pytorch
- policy-evaluation
- simulation