Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 15 | Automated Pour-over Coffee Machine with Imitation Learning |
Jie Wang Jingyuan Huang Rucheng Ke William Qiu |
design_document3.pdf final_paper3.pdf photo1.jpg proposal2.pdf |
Said Mikki | |
| # RFA for Automated Pour-over Coffee Machine with Imitation Learning # Problem The art of pour-over coffee brewing, famous for its complex flavor and high quality, is heavily dependent on the skills and experience of a barista. This craftsmanship leads to variability in coffee quality due to human inconsistency. Additionally, it is challenging for common coffee enthusiasts to replicate professional barista techniques at home or in non-specialized settings. # Solution Overview We propose the development of **an intelligent Automated Pour-over Coffee Machine leveraging imitation learning algorithms**. This machine will mimic the techniques of professional baristas, ensuring consistency and high-quality in every cup. The project will involve designing a mechanical structure integrated with sensors and developing sophisticated software algorithms. # Solution Components ## Component 1: Mechanical Design - **Purpose:** To create a machine that can physically replicates the movements and precision of a barista. - **Features:** An adjustable nozzle for water flow control, a mechanical arm for simulating hand movements, and a stable structure to house the coffee dripper. - **Challenges:** Ensuring precise movement and durability of moving parts, and integrating the mechanical system with electronic controls for seamless operation. - **Expectation:** A workable, fixed coffee machine first, then upgrade it. ## Component 2: Sensors and Data Collection - **Purpose:** To gather precise data on barista techniques for the learning algorithm. - **Features:** High-precision sensors capturing data on water flow, angle, speed, and trajectory during the pour-over process. - **Challenges:** Accurately capturing the nuanced movements of a professional barista and ensuring sensor durability under varying conditions. ## Component 3: Imitation Learning Algorithm - **Purpose:** To analyze and learn from the collected data, enabling the machine to replicate these actions. - **Features:** Advanced algorithms processing visual and sensory data to mimic barista techniques, this requires to duplicate the state-of-the-art research result from Robotics field. - **Challenges:** Developing an algorithm capable of adapting to different styles and ensuring it can be updated as it learns from new data. ## Optional Components: - **Multimodal Origin Information Pre-Processing:** To adjust settings based on different coffee beans and grind sizes. - **User Interface Design:** An intuitive interface for user customization and selection of coffee preferences. - **ChatGPT Enhanced Custom Coffee Setting**: To make the machine more intelligent and like a human barista, SOTA artificial intelligence like LLMs should be involved to make it more a sort of an agent than a regular machine. # Criterion for Success - **Mechanical Precision:** The machine must accurately control water flow and replicate barista movements. - **Algorithm Effectiveness:** The machine should consistently brew coffee that matches or surpasses the quality of a professional barista. - **User Experience:** The interface should be user-friendly, allowing customization without overwhelming the user. - **Reliability and Durability:** The machine should operate consistently over time with minimal maintenance. - **Taste Test Approval:** The coffee produced must be favorably reviewed in taste tests against traditional pour-over coffee. |
|||||