Architectural Robotics with Perception

This course follows 48-555/48-755 Introduction to Architectural Robotics. The course teaches how robots sense, interpret, and act in the built environment, connecting design intent to fabrication. Students learn the fundamentals of camera and depth, real-time sensor-based system workflow, and the deployment of useful techniques. Perception streams are integrated into ROS and MoveIt to build planning scenes, manage frames, and execute perception-guided motion. The labs prioritize reproducible workflows and clear documentation, enabling methods that can be transferred to studio, research, and thesis work. The final project invites students to apply a sensing-to-action pipeline to a problem in architectural robotics, such as timber assembly, fixtureless positioning, or quality assessment, using their own data and constraints.

Instructor: Jiaying Wei

Term: Spring

Location: MMCH,dFab RobotRoom

Time: Mondays and Wednesdays, 2:00-3:50 PM

Course Overview

This course provides a comprehensive introduction to data science principles and practices. Students will:

  • Learn the end-to-end data science workflow
  • Gain practical experience with data manipulation tools
  • Develop skills in data visualization and communication
  • Apply statistical methods to derive insights from data

Prerequisites

  • Basic programming knowledge (preferably in Python)
  • Introductory statistics
  • Comfort with basic algebra

Textbooks

  • “Python for Data Analysis” by Wes McKinney
  • “Data Science from Scratch” by Joel Grus

Grading

  • Assignments: 50%
  • Project: 40%
  • Participation: 10%

Schedule

Week Date Topic Materials
1 Feb 5 Introduction to Data Science

Overview of the data science workflow and key concepts.

2 Feb 12 Data Collection and APIs

Methods for collecting data through APIs, web scraping, and databases.

3 Feb 19 Data Cleaning and Preprocessing

Techniques for handling missing values, outliers, and data transformation.

4 Feb 26 Exploratory Data Analysis

Descriptive statistics, visualization, and pattern discovery.

5 Mar 4 Statistical Analysis

Hypothesis testing, confidence intervals, and statistical inference.

6 Mar 11 Data Visualization

Principles and tools for effective data visualization.