Overview
What this challenge is about.
Train a diffusion policy on bimanual cooking demos, beat a BC-MLP baseline, and analyze both solution modes. Earn a verifiable certificate.
The scenario
The startup (around 25 staff, post-Seed) is targeting eventual consumer kitchen robots and needs early evidence that diffusion policies handle multimodal demos well; the result feeds the next research-roadmap update.
The Brief
What you'll do, and what you'll demonstrate.
Train a diffusion policy on bimanual pour-and-stir demos and prove it handles multimodal solution distributions better than a BC-MLP baseline.
Earning criteria — what you'll demonstrate
- Implement a diffusion-policy action head for imitation
- Design experiments that surface multimodality (mode coverage, not just success)
- Compare diffusion and BC fairly on the same demos
- Write a research note that proposes the next 3 experiments
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Robot Learning
Master · Applied Ai
Strong alignment
This challenge maps to Robot Learning 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.
- Diffusion Policies
Apply diffusion policies to solve real industry problems and demonstrate production-level capability.
- Imitation Learning
Apply imitation learning to solve real industry problems and demonstrate production-level capability.
- Multimodal Action Distributions
Apply multimodal action distributions to solve real industry problems and demonstrate production-level capability.
- Manipulation
Apply manipulation to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch 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:
ML Researcher
Implementing diffusion policies and proving multimodality wins is exactly the research-engineering work robot-learning labs hire for in 2024-25.
This challenge sharpens
- diffusion-policies
- imitation-learning
- multimodal-action-distributions
Research Scientist
Designing experiments that surface a method's specific advantage (mode coverage) is the kind of research-design skill industrial research scientists need.
This challenge sharpens
- diffusion-policies
- policy-evaluation
- multimodal-action-distributions
Applied AI Scientist
Translating a research result into 3 concrete follow-up experiments is the applied-AI-scientist craft of moving research toward product.
This challenge sharpens
- imitation-learning
- manipulation
- policy-evaluation