Course Project

The primary objective of the course project is to give you hands-on experience implementing computational robotics algorithms and systems. The project will allow you to apply the theoretical concepts learned in class to practical problems in robotics. You are expected to work in teams of 2-3 students, and grades will be calibrated by team size.

Project Components and Timeline

The project consists of several milestones throughout the semester:

  • Project Proposal (5%): Due Week 5 (Sep 29)
    • 2-3 page proposal describing your project idea
    • Clear problem statement and objectives
    • Technical approach and methodology
    • Expected outcomes and evaluation metrics
  • Milestone 1 (5%): Due Week 9 (Oct 22)
    • Initial implementation progress
    • Preliminary results and challenges
    • Updated timeline and next steps
    • 2-3 pages
  • Milestone 2 (5%): Due Week 13 (Nov 17)
    • Significant progress on core functionality
    • Intermediate results and analysis
    • Final implementation plan
    • 2-3 pages
  • Project Presentations (10%): Weeks 15-16 (Dec 1-8)
    • Presentation of your project
    • Live demonstration of your system
    • Q&A session with instructors and peers
  • Final Report (15%): Due Dec 15
    • Comprehensive technical report (8 pages)
    • Complete implementation details and results
    • Analysis, discussion, and future work

Suggested Project Topics

Here is a list of suggested project topics organized by the main areas covered in the course. You are welcome to discuss with instructors and TAs if you have other interesting topics you want to work on.

  • Kinematics and Motion Planning
    • Implementation of forward/inverse kinematics for custom robot arms
    • Motion planning algorithms (RRT, PRM, A*) for different environments
    • Multi-robot coordination and path planning
    • Trajectory optimization and smoothing
  • Control Systems
    • PID controller implementation and tuning
    • Impedance control for human-robot interaction
    • Model predictive control for robotic systems
    • Adaptive control algorithms
  • Computer Vision and Perception
    • Camera calibration and 3D reconstruction
    • Object detection and pose estimation
    • SLAM implementation for mobile robots
    • Integration of foundation models (SAM, DINO) for robotics
  • Machine Learning for Robotics
    • Imitation learning from demonstrations
    • Reinforcement learning for robot control
    • Learning robot dynamics models
    • Multi-modal learning for robotic tasks
  • System Integration
    • Complete robotic system with multiple components
    • Simulation-to-real transfer
    • Human-robot interaction systems
    • Multi-robot systems and coordination

Project Requirements

  • Implementation: Your project must include substantial implementation work, not just theoretical analysis
  • Documentation: Clear documentation of your approach, implementation, and results
  • Evaluation: Quantitative evaluation of your system's performance
  • Innovation: Demonstrate creativity in problem-solving or implementation
  • Presentation: Effective communication of your work through presentation and report

Getting Started

To help you get started with your project:

  • Review the course materials and identify areas that interest you most
  • Consider your team's strengths and interests
  • Start with a well-defined, achievable scope
  • Plan for incremental development and testing
  • Utilize available computing resources (Google Cloud, Colab)
  • Schedule office hours with instructors and TAs for guidance

Remember that the best projects often start simple and evolve based on what you learn during implementation. Focus on building a solid foundation and then iteratively improve your system.

Related Materials and Resources

Below are some useful resources to help you with your course project: