Capstone Project: Textbook Contribution & Implementation

Objective

Make a substantive contribution to the VLA Foundations textbook by authoring technical content, implementing code, and presenting your work to the class.

Learning Goals

• Synthesize knowledge from multiple research papers
• Implement a non-trivial VLA component or experiment
• Communicate technical concepts clearly in writing
• Present findings to a technical audience

Project Tracks

Choose one of the following tracks:

Track 1: Research Extension

Extend an existing VLA paper with novel experiments or analysis.

Requirements:

• Reproduce baseline results from a published paper
• Design and run new experiments that test an unexplored dimension
• Contribute a textbook section analyzing your findings

Example Projects:

• "Does RT-2 generalize to novel object geometries?" - Test on CAD-generated objects
• "Scaling laws for VLA data diversity" - Ablate dataset composition
• "Failure modes of diffusion policies in cluttered scenes" - Systematic failure analysis

Track 2: Engineering Implementation

Build a production-grade VLA component from scratch.

Requirements:

• Implement a key VLA technique (encoder, policy, training pipeline)
• Write clean, documented, tested code
• Contribute a textbook section with implementation details

Example Projects:

• "Efficient vision encoder for real-time robotic control" - Optimized transformer
• "Multi-task policy training framework" - PyTorch training harness
• "Sim-to-real transfer toolkit" - Domain randomization + evaluation suite

Track 3: Comprehensive Survey

Write an authoritative survey of a VLA subtopic.

Requirements:

• Read 15-20 papers in a focused area
• Identify trends, gaps, and open questions
• Contribute a textbook section synthesizing the literature

Example Projects:

• "Data augmentation strategies for robotic learning" - Survey + taxonomy
• "Benchmarking protocols for manipulation tasks" - Analysis of evaluation methods
• "Foundation models for embodied AI: A critical review" - Strengths/weaknesses analysis

Deliverables

1. Proposal (Week 8)

Submit: 1-2 page proposal via pull request

Contents:

• Track selection (Research/Engineering/Survey)
• Problem statement and motivation
• Planned approach and timeline
• Expected contribution to textbook

Grading: Pass/Fail (instructor feedback provided)

2. Textbook Chapter Contribution (Week 16)

Submit: MDX file with written content

Requirements:

• 2000-4000 words of technical writing
• LaTeX equations for mathematical formulations
• Code snippets (if applicable)
• References to relevant papers
• Fits cohesively into one of the 8 textbook chapters

Location: content/textbook/[chapter-name]/your-section.mdx

Example Structure:

---
title: "3.5 Your Section Title"
chapter: 3
subsection: 5
author: "Your Name"
---

# 3.5 Your Section Title

## Motivation

Why does this topic matter?

## Background

What do readers need to know?

## Method

How does it work? (Include equations)

## Results

What did you find? (Include figures/tables)

## Discussion

What are the implications?

## References

[Numbered references]

3. Code Implementation (Weeks 12-16)

Required for Research & Engineering tracks (optional for Survey track)

Submit: Pull request with code

Requirements:

• Clean, documented Python code
• README with setup instructions
• Example usage / demo script
• Unit tests (if applicable)

Location: code/capstone/your-project-name/

Grading Criteria:

• Code quality and organization (30%)
• Documentation and comments (30%)
• Functionality and correctness (40%)

4. Final Presentation (Week 16)

Format: 15-minute presentation + 5-minute Q&A

Contents:

1. Problem statement and motivation (2 min)
2. Approach and methodology (5 min)
3. Results and findings (5 min)
4. Textbook contribution overview (2 min)
5. Lessons learned and future work (1 min)

Slides: Submit PDF via pull request

Timeline

Week	Milestone
8	Proposal due
10	Progress check-in (office hours)
12	Draft textbook section (optional feedback)
14	Code implementation complete
16	Final presentation + all deliverables due

Grading Rubric (300 points)

Component	Points
Textbook Contribution	150
- Technical accuracy	50
- Writing clarity	40
- Integration with existing chapters	30
- References and citations	30
Implementation / Code	100
- Functionality	40
- Code quality	30
- Documentation	30
Presentation	50
- Content clarity	20
- Slide quality	15
- Q&A responses	15
Total	300

Evaluation Criteria

Textbook Contribution

Excellent (90-100%):

• Novel insights or analysis
• Crystal-clear explanations
• Publication-quality figures and equations
• Comprehensive references

Good (80-89%):

• Accurate technical content
• Clear writing with minor issues
• Relevant figures and equations
• Adequate references

Acceptable (70-79%):

• Mostly accurate content
• Understandable but needs polish
• Basic figures/equations
• Some key references missing

Code Implementation

Excellent (90-100%):

• Production-ready code
• Comprehensive documentation
• Runs out-of-the-box
• Includes tests and examples

Good (80-89%):

• Functional code
• Adequate documentation
• Minor setup issues
• Basic examples

Acceptable (70-79%):

• Code works with effort
• Minimal documentation
• Requires debugging
• No examples

Presentation

Excellent (90-100%):

• Engaging and clear
• Well-structured slides
• Confident Q&A responses
• On time

Good (80-89%):

• Clear presentation
• Decent slides
• Handles most questions
• Slightly over/under time

Acceptable (70-79%):

• Understandable content
• Basic slides
• Struggles with some questions
• Noticeable timing issues

Submission Process

Proposal (Week 8)

git checkout -b capstone-proposal-yourname
# Add file: content/course/proposals/yourname-proposal.md
git add content/course/proposals/yourname-proposal.md
git commit -m "Add capstone proposal: Your Name"
git push origin capstone-proposal-yourname
# Open PR to staging

Final Submission (Week 16)

git checkout -b capstone-final-yourname
# Add textbook section: content/textbook/[chapter]/your-section.mdx
# Add code (if applicable): code/capstone/your-project/
# Add slides: presentations/yourname-final.pdf
git add .
git commit -m "Add capstone project: Your Title"
git push origin capstone-final-yourname
# Open PR to staging

Example Projects from Past Semesters

Research Track

"Generalization of RT-2 to Novel Objects" (Jane Doe, 2025)

• Reproduced RT-2 baseline on Open-X dataset
• Generated 50 novel 3D objects with unseen geometries
• Found 23% performance drop on novel objects
• Contributed to Chapter 4 (Evaluation)

"Data Augmentation for Robotic Grasping" (John Smith, 2025)

• Implemented 8 augmentation strategies
• Trained policies with systematic ablations
• Identified that rotation augmentation improves generalization by 15%
• Contributed to Chapter 2 (Data)

Engineering Track

"Real-Time Vision Encoder for Edge Deployment" (Alice Johnson, 2025)

• Implemented MobileViT-based encoder
• Achieved 30 FPS on Jetson Orin
• Only 5% accuracy drop vs. ViT-B
• Contributed to Chapter 5 (Deployment)

"Multi-Task Policy Training Framework" (Bob Williams, 2025)

• Built PyTorch training harness for 10+ tasks
• Supports multi-GPU, checkpointing, logging
• Open-sourced with 500+ GitHub stars
• Contributed to Chapter 3 (Training)

Survey Track

"Benchmarking Protocols for Manipulation" (Carol Lee, 2025)

• Analyzed 30 papers on manipulation benchmarks
• Created taxonomy of evaluation metrics
• Identified reproducibility issues in 60% of papers
• Contributed to Chapter 4 (Evaluation)

"Foundation Models for Embodied AI: A Survey" (David Chen, 2025)

• Surveyed 40 papers on VLMs for robotics
• Mapped landscape of architectures and datasets
• Identified key open problems
• Contributed to Chapter 7 (Future Directions)

Resources

Writing

• How to Write a Great Research Paper
• LaTeX Math Symbols
• MDX Documentation

Code

• Google Python Style Guide
• PyTorch Best Practices
• Writing Good Documentation

Presentation

• How to Give a Great Research Talk
• Presentation Tips

FAQs

Q: Can I work in a team? A: No, capstone projects must be individual work. However, you can discuss ideas with classmates.

Q: Can I extend my paper audit into a capstone? A: Yes! If you found an interesting research question during a paper audit, you can explore it further.

Q: What if my code doesn't work perfectly? A: Document what worked, what didn't, and why. Partial results are acceptable if well-analyzed.

Q: Can I contribute to multiple textbook chapters? A: Focus on one cohesive section. Quality over quantity.

Q: What if my project scope changes? A: Discuss with the instructor. Pivots are allowed with justification.

Getting Help

• Office Hours: Every Tuesday/Thursday 3-4 PM
• Discussion Forum: Post questions and get peer feedback
• Mid-Project Check-In: Schedule a meeting in Week 12

Final Notes

The capstone is your opportunity to make a lasting contribution to the VLA research community. Past student projects have been cited in papers, used by other researchers, and featured in the textbook for future cohorts.

Aim for work you'd be proud to showcase in a job interview or PhD application.

Good luck!