Topic 5 — Documentation, Reflection & Future Work
The final step of your capstone is to package your work so that others can understand, run, and extend it. This topic focuses on documentation, reflection, and identifying future directions—turning your project from a one‑off demo into a stepping stone for future research or product development.
5.1 Project Documentation Essentials
At minimum, your repository should include:
- README / Project Overview:
- One-paragraph description of the project.
- Key capabilities and scenarios.
- High-level diagram or link to architecture docs.
- Setup & Run Instructions:
- Software and hardware prerequisites.
- Installation steps and environment setup.
- Commands to launch the simulation and run the main demo.
- Structure & Components:
- Brief explanation of important packages, nodes, and scripts.
- Pointer to detailed docs for perception, planning, and control stacks.
Consider adding a short “Quickstart Demo” section that new users can run in under 10–15 minutes.
5.2 Technical Report or Final Write-Up
Your report should mirror a concise research or engineering paper:
- Introduction:
- Problem statement, motivation, and context within Physical AI.
- Summary of your proposed system and contributions.
- System Design:
- Architecture overview and design choices.
- Key algorithms and implementation details (with references to earlier chapters where appropriate).
- Experiments & Results:
- Scenarios tested, metrics, and outcomes.
- Plots, tables, and qualitative observations.
- Discussion:
- Strengths, limitations, and lessons learned.
- Failure cases and what they reveal about your design.
- Conclusion & Future Work:
- Summary of what you achieved.
- Concrete next steps or research questions.
Keep the report focused and readable; aim for clarity over exhaustive detail.
5.3 Reflection: What You Learned
Spend time reflecting on:
- Technical Growth:
- Which parts of the stack you understand deeply now (ROS 2, simulation, perception, planning, multi-agent coordination).
- Which areas remain challenging or confusing.
- Engineering Practice:
- How architecture, testing, and iteration affected your final outcome.
- What you would do differently if starting again.
- Team Skills (if applicable):
- Communication, task division, and collaboration.
- How you handled disagreements or scope changes.
Capture these reflections in a short document or appendix to your report.
5.4 Future Directions
Connect your capstone to future work:
- Technical extensions:
- Richer environments, more complex tasks, or additional robots.
- Stronger perception models, improved planners, or better human-robot interfaces.
- Cloud-based deployment, remote operators, or large-scale fleet experiments.
- Research questions:
- What open problems did you encounter (e.g., sim‑to‑real gaps, multi-agent coordination challenges, safety guarantees)?
- How might your system serve as a testbed for these questions?
- Productization:
- How could this evolve into a lab platform, startup idea, or open-source project?
Write these as concrete bullet points so they are easy to revisit later.
5.5 Mini-Lab: Final Packaging
Goal: Ship a capstone that is easy to run, understand, and build upon.
Tasks
- Complete and polish your README and documentation.
- Finish your final report and reflection.
- Verify that a fresh clone of your repository can run the demo by following your own instructions step-by-step.
Deliverables
- Updated repository with clear documentation and a reproducible demo.
- Final report (and optional slide deck or video) summarizing your work and findings.
Summary
Documentation, reflection, and future work transform your capstone from a one-time achievement into a launchpad for ongoing learning, research, and real-world impact. Together with the previous topics in Chapter 7, you now have a complete blueprint for designing, executing, and presenting a full-stack Physical AI humanoid project.