top of page

INFO 5356-030: Introduction to Human-Robot Interaction

Cornell Tech
Fall 2026, 2025, 2024, 2022

Course Description

Robots are making their way into our everyday lives, working across many applications, including in people’s homes, healthcare, and retail settings. As robotic systems become more integrated into our lives, they must be designed to be useful, functional, and socially acceptable; however, this remains a key challenge for the field of human-robot interaction (HRI). This course covers core computational, engineering, social, robot design, and prototyping; as well as important topics in HRI–including, research methods, anthropomorphism, robot perception of people, human perception of robots, generative AI methods, and social signal processing – recognition and synthesis for HRI. This course endeavors on the end-to-end HRI pipeline to understand the impacts of robot design, algorithms, model training, and evaluation procedures including qualitative and quantitative methods and analyses. The final project challenges student teams to develop an interactive robotic game for K-12 students, demonstrating an understanding of robot design, prototyping, development, and iterative assessment of human-human and human-robot interactions. After taking this course, students will have the critical thinking skills to propose, introduce, and execute human-robot interaction studies, including tightly coupled software and hardware constraints on HRI to produce scientifically sound outcomes. Furthermore, students can expect to learn about seminal research in HRI, gain hands-on experience with physical tabletop robots, and implement systems for real-time interaction with people.  

Prerequisites: CS 2800 or equivalent, experience programming in Python, or permission of the instructor.

Reading: Bartneck, Christoph, Tony Belpaeme, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Šabanović. Human-robot interaction: An introduction. Cambridge University Press, 2020.

Introduction to HRI Course

Learning Outcomes

  • Program simulated and physical tabletop robots to perform complex verbal and non-verbal behaviors in HRI scenarios. 

  • Engage in robot design and prototyping (e.g., laser cutting and 3D printing) of new robot embodiments and behaviors.

  • Understand important topics in HRI and the impact of robots in real-world settings.

  • Design a gamified user study to measure and evaluate the robot’s effectiveness and user perceptions of it during field deployments.

Introduction to HRI Course

Course Schedule

Screenshot 2025-08-21 at 5.52.34 PM.png

Introduction to HRI Course, 2026

Robotic Platform for Teaching

reachy mini.jpeg

REACHY MINI

An expressive companion robot designed for human interaction, creative coding, and AI experimentation.

The HRI course utilized the Reachy Mini platform that is 11 inches tall, built to sit on a tabletop, built for AI research, education, and human connection .

Introduction to HRI Course, 2025-22

Robotic Platform for Teaching

TURTLEBOT4

9kg

Payload Capacity

The HRI course utilized the TurtleBot 4 platform that is built on the iRobot® Create 3 educational robot – a sturdy mobile base that provides an array of intelligent sensors for accurate localization and positioning with a speed up to .3 m/s. The Create 3 has a standalone Robot Operating 2 interface and unlike previous TurtleBots, it includes integrated batteries and a charging dock.

Introduction to HRI Course, 2025-23

Final Projects

We organized a K-12 event in collaboration with the Cornell Tech K-12 initiative team including Diane Levitt, Sophie Lachez, and Meg Ray. 50 NYC Dock Street School students from predominantly underserved communities participated in a 3-hour event. Students in the HRI course demonstrated cumulative knowledge at this event including programming the robot to perform complex behaviors, designing and prototyping a robotic embodiment to capture the role the robot plays in the game, demonstrating their robot with four groups of middle school students, and administering study surveys after games to evaluate how the participants perceived their robot.

Dr. Robot

The robot advances through a map of the human body when participants correctly respond to questions about the human body in a web-based interface, inspired by the game 'Operation'.

dr_robot.gif
BW-7033.jpg
BW-7033.jpg
IMG_1527.HEIC

Mario’s Coin Quest

The robot moves left or right at based on 3D red and green blocks on the ground, and collects coins along the way. The player with the most coins wins!

marios_coin_quest.gif
Screen Shot 2024-12-21 at 12.23.07 AM.png
Screen Shot 2024-12-21 at 12.23.07 AM.png
BW-6905.jpg

Pong Soccer

The blue and yellow teams throw balls into the blue and yellow side of the soccer ball to make the robot advance to their goal.

pong_soccer.gif
BW-6950.jpg
Screen Shot 2024-12-21 at 12.21.08 AM.png

Rat Roulette

The robot splits to select a number at a fixed probability and navigates to the participant standing at the selected number and color (red and black) to give them a block of cheese.

rat_roulette.gif
BW-6860.jpg
BW-6919.jpg

RoboSketch

The robot follows a participant to draw a shape on the ground.

robosketch.gif
BW-6883.jpg
BW-6880.jpg
BW-6964.jpg
BW-6920.jpg

Red Light, Green Light

The robot says ‘red light’ and participants stop moving, and it says ‘green light’ for participants to move forward. If you move during ‘red light’, you are out!

greenlight_redlight.gif
Screen Shot 2024-12-21 at 12.24.37 AM.png
Screen Shot 2024-12-21 at 12.24.37 AM.png
BW-6915.jpg

Cat and Mouse

The robot rotates and stops randomly. If you are caught in front of the robot, you are out!

tomtarget.gif
BW-6967.jpg
BW-6967.jpg
BW-6957.jpg

Time's Up!

The robot plays music and participant teams guess where the song comes from using flash cards. The robot detects QR codes to recognize correct or incorrect robot music references. Participants that select the correct music references win!

times_up.gif
Screen Shot 2024-12-21 at 12.28.48 AM.png
Screen Shot 2024-12-21 at 12.28.48 AM.png
BW-7038.jpg

Credits: We thank Niti Parkih, the Cornell Tech MakerLab Director, for providing educational tools for digital fabrication, and William Leon for creating 3D models of the final project robots.

bottom of page