[2016] DIOD An interactive lighting installation using Kinect to
map topologies and project levels with a projector.
[2016] Weti A responsive environmental system using Arduino and
a moisture sensor to monitor the clay’s hydration levels.
Tools & Software Development
[2026] Rhino Illustrator Bridge (RIB) A Rhino 8 plugin for connecting Rhino geometry and Adobe Illustrator workflows
Authors: Hamid Peiro
Link to RIB on Food4Rhino [2021] JAVID: A Grasshopper Plugin for Image ProcessingAn image-processing plugin for Grasshopper that generates artistic two-dimensional graphical images from bitmap files.
Link to JAVID on Food4Rhino [2021] IAC: A Grasshopper Plugin for Mathematics and GeometryA Grasshopper plugin featuring components for mathematics and geometry, designed to enhance data processing speed and address missing functionalities in Grasshopper workflows.
Authors: Mahdiyar Esmailbeigi, Parisa Afshari, Ghazaleh Elyasi, Maedeh Fallah, Mohammad Mehdi Jalali, Atena Meshkat, Laleh Moradi, Hamid Peiro, Ali Sa’adati, Helia Zakeri, Fatemeh Zarei
Hardware II “ Computer vision, robotics, and AI-driven fabrication systems“
Hardware II
MAA01 · MRAC01 · 2025–26
Hardware II explores the application of computer vision and artificial intelligence to architectural, robotic, and interactive systems. The course introduces students to the complete AI vision pipeline, from data collection and model training to real-time perception, spatial analysis, and deployment in physical environments.
Students work with tools including YOLO, OpenCV, ONNX, and ROS2 to develop systems that can perceive, interpret, and respond to their surroundings. The course focuses on transforming visual information into spatial intelligence and connecting AI-based perception with robotic control, Arduino, and interactive architectural systems.
The course culminates in independent projects that integrate computer vision, AI, decision-making, and physical actuation, with an emphasis on building complete and explainable systems rather than isolated computational experiments.
Hamid Peiro contributed as Faculty, supporting students in computer vision, AI-based perception, Python programming, robotic integration, ROS2, and the development of autonomous physical systems.
Faculty: Hamid Peiro
Faculty Assistant: Aleksandra Kraeva
Institution: Institute for Advanced Architecture of Catalonia (IAAC)
Selected Student Projects
Human Trace — Autonomous Single-Stroke Robotic Sketching
Human Trace explores how computer vision can be translated into continuous physical movement through robotic drawing. A webcam captures a face or object, which is processed using OpenCV to extract visual features and generate drawable geometry.
The project develops a computational pipeline that transforms image contours into a continuous path, optimizes the sequence of points, maps the geometry into robotic coordinates, and generates an uninterrupted robotic trajectory. The result connects vision, image processing, computational geometry, and robotic motion to transform digital information into a physical trace.
Vision-Based Gesture-Controlled Robotic Manipulation System
This project develops a vision-based human–robot interaction system that allows a user to control a UR10e industrial robot through hand gestures. The system combines workspace perception, object detection, gesture recognition, and robotic execution into a single pipeline.
An overhead camera detects objects and establishes their spatial coordinates, while a second camera uses MediaPipe Hands to interpret the user's gestures. The resulting commands are transferred through a ROS2-based communication system and converted into robotic pick-and-place actions. The project demonstrates how computer vision can provide a natural interface between human gestures and industrial robotic manipulation.
Students: Rafik El Khoury · Leonard Elias Böker · Elias El Asmar · Seid Burka · Dhruvil Bhanushali
CIRCUIT — Circular Intelligence for Robotic Classification & Upcycling of Industrial Timber
CIRCUIT develops a vision-guided robotic system for identifying, measuring, and sorting discarded timber components for potential reuse. A camera-based perception system detects individual wooden elements and estimates their position, orientation, and dimensions.
The detected information is transferred into Grasshopper, where computational rules identify the timber pieces that satisfy user-defined requirements. A robotic arm then selects and manipulates the appropriate components, creating a complete workflow from computer vision and spatial analysis to parametric decision-making and robotic execution. The project demonstrates how AI and robotics can support material reuse and automated construction workflows.