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_document1.pdf
design_document2.pdf
design_document3.pdf
proposal1.pdf
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.

Intelligent Texas Hold 'Em Robot

Xuming Chen, Jingshu Li, Yiwei Wang, Tong Xu

Featured Project

## Problem

Due to the severe pandemic of COVID-19, people around the world have to keep a safe social distance and to avoid big parties. As one of famous Poker games in the western world, the Texas Hold’em is also influenced by the pandemic and tends to turn to online game platform, which, unfortunately, brings much less real excites and fun to its players. We hope to develop a product to assist Poker players to get rid of the limit of time and space, trying to let them enjoy card games just as before the pandemic.

## Solution Overview

Our solution is to develop an Intelligent Texas Hold’em robot, which can make decisions in real Texas poker games. The robot is expected to play as an independent real player and make decisions in game. It means the robot should be capable of getting the information of public cards and hole cards and making the best possible decisions for betting to get as many chips as possible.

## Solution Components

-A Decision Model Based on Multilayer Neural Network

-A Texas Hold'em simulation model which based on traditional probabilistic models used for generating training data which are used for training the decision model

-A module of computer vision enabling game AI to recognize different faces and suits of cards and to identify the game situation on the table.

-A manipulation robot hand which is able to pick, hold and rotate cards.

-Several Cameras helping to movement of robot hand and the location of cards.

## Criterion for Success

- Training a decision model for betting using deep learning techniques (mainly reinforcement learning).

- Using cv technology to transform the information of public cards and hole cards and the chips of other players to valid input to the decision-making model.

- Using speech recognition technology to recognize other players’ actions for betting as valid input to the decision model.

Using the PTZ to realize the movement of the cameras which are used to capture the information of pokers and chips.

- Finish the mechanical design of an interactive robot, which includes actions like draw cards, move cards to camera, move chips and so on. Utilize MCU to control the robot.

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