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MATH 202: Physics of Robots
Course Code MATH 202
Course Name Physics of Robots
Department Mathematics
Semester Offered Odd (usually Semester 3)
Tuition Hours 30 hours
Course Level Intermediate
Pre-requisite MATH 104: General Physics
Co-requisite -
Course Objective Software is easy because the world inside your computer obeys you. Hardware is hard because the real world does not.

This course is about closing that gap. It connects the clean abstractions of mathematics and code with the messy reality of motion, friction, voltage, noise, and heat.

Students will learn how to model, reason about, and control physical systems, from simple circuits to moving robotic systems. The goal is not to turn you into a mechanical engineer, but to make sure that when you build AI systems that touch the real world, you actually understand what is going on underneath.

By the end, a robot or any hardware system should not feel magical. It should feel like a system you can break down, model, and improve.
Course Philosophy This course emphasizes
  • Build first, then derive
  • Physical intuition over formula memorization
  • Systems thinking over isolated components
You will not learn physics as separate chapters. You will learn it the way builders do, by asking: why is my system failing, and what does physics say about it?
Course Learning Outcomes Upon successful completion of this course, students will be able to:
  • Model basic robotic motion using principles of mechanics such as force, torque, and energy.
  • Understand and design simple electrical systems, including circuits involving resistors, capacitors, and voltage sources.
  • Work with sensors and actuators, and interpret noisy real-world signals.
  • Analyze feedback systems and understand stability, control, and error correction.
  • Reason about real-world constraints such as friction, heat, power consumption, and material limits when building hardware systems.
  • Bridge software and hardware thinking, enabling them to build AI systems that operate outside the browser.
Course Author Sagar Udasi
MSc Statistics and Data Science with Computational Finance from The University of Edinburgh.
Contact: sagar.l.udasi@gmail.com
Course Organiser TBD
No. Lecture Title Concepts Covered Lecture Objective
01 Why Robots Fail in the Real World Difference between simulation and reality, noise, uncertainty Students understand why physics matters when moving from code to hardware systems
02 Motion Is Never Free Kinematics, displacement, velocity, acceleration Build intuition for how robots move and how to model that motion
03 Forces That Actually Matter Newton’s laws, friction, drag, real-world forces Learn to account for forces that affect stability and movement of hardware
04 Turning Motion Into Control Torque, rotational dynamics, motors Understand how motors translate electrical input into physical motion
05 The Hidden Cost of Movement Work, energy, power consumption Learn to optimize systems for efficiency, battery life, and sustainability
06 Circuits: The Nervous System of Robots Voltage, current, resistance, Ohm’s law Build foundational understanding of how electronic systems function
07 When Circuits Remember Capacitors, basic filtering, transient behavior Understand timing, smoothing, and energy storage in circuits
08 Sensors: How Machines See the World Types of sensors, signal acquisition, noise Learn how real-world data is captured and why it is messy
09 From Signal to Meaning Signal processing basics, filtering, thresholding Convert raw sensor data into usable inputs for AI systems
10 Actuators: From Code to Action Motors, servos, control signals Bridge the gap between software decisions and physical execution
11 Feedback Is Everything Feedback loops, PID control basics Understand how systems self-correct and maintain stability
12 When Systems Oscillate and Break Stability, resonance, failure modes Diagnose and prevent unstable or oscillating systems
13 Heat, Stress, and Failure Thermal effects, material limits Learn how physical constraints affect long-term system reliability
14 Power Is Your Real Constraint Power systems, battery limitations Design systems that operate within real-world energy constraints
15 Modeling a Simple Robot System-level modeling combining mechanics and circuits Integrate multiple concepts into a working mental model
16 Why Your Robot Won’t Stand Balance, center of mass, stability Apply physics to solve real mechanical challenges
17 From Open Loop to Closed Loop Systems Control systems in practice Transition from naive systems to robust, adaptive ones
18 Debugging Hardware Like an Engineer Measurement, testing, iteration Learn practical debugging strategies for physical systems
19 Building Under Constraints Trade-offs between cost, performance, reliability Prepare students for real-world engineering decisions
20 From Physics to Product Integrating hardware with AI systems Connect course learning directly to building wearable or robotic prototypes
Component Weightage
Practical Lab Assignments (3 total) 40%
Mini Hardware Project 30%
Written Examination (2 hours) 30%
Type Resource Provider
Lecture MIT 2.003SC Engineering Dynamics MIT OpenCourseWare
Lecture Circuits and Electronics MIT OpenCourseWare
Lecture Control Systems Lectures Brian Douglas (YouTube)
Reading The Art of Electronics Horowitz & Hill
Reading Introduction to Robotics: Mechanics and Control John J. Craig