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globalThis;try{return this||new Function("return this")()}catch(e){if("object"==typeof window)return window}}(),a.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},r={},o="robot-intel-fl23:",a.l=function(e,t,n,c){if(r[e])r[e].push(t);else{var f,u;if(void 0!==n)for(var i=document.getElementsByTagName("script"),d=0;d Assignments | Introduction to Robot Intelligence - +

Homework 0 - Preliminaries for the class.

LaTeX Template

Homework 1

Theory Questions

Coding Questions

Homework 2

Project Homework

Homework 3

Theory Questions

Coding Questions

Homework 4 and more

  • Coming soon.
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Archive

Archive

2021

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Tags

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One post tagged with "review"

View All Tags
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One post tagged with "robotics"

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review

- + \ No newline at end of file diff --git a/index.html b/index.html index 19326e5a..d2d8f9db 100644 --- a/index.html +++ b/index.html @@ -6,13 +6,13 @@ Hello from Introduction to Robot Intelligence | Introduction to Robot Intelligence - +

Introduction to Robot Intelligence

Fall 2023

Why take this course?

Robotics is a transformative field at the intersection of engineering, artificial intelligence, and automation, focused on designing, constructing, and operating mechanical agents known as robots. At its core lies the aspiration to imbue these machines with a semblance of intelligence, enabling them to perceive, reason, and act autonomously within their environments. Robot intelligence encompasses a spectrum of capabilities, from basic sensor-based tasks to complex decision-making processes. There are many applications of robotics riddled with open problems, including space exploration, self-driving vehicles,warehouse robots and general-purpose home assistants. In this class, we will take the first steps towards understanding the design of robots that can think and act, and learn what is about these open problems that makes them so challenging. Throughout the course, students will be tasked with the captivating challenge of assembling and integrating basic electronic components onto a rover platform, transforming theoretical concepts into tangible creations. By immersing themselves in this practical endeavor, students will not only gain proficiency in electronics, mechanics, and programming, but also cultivate problem-solving skills essential for tackling real-world robotics problems. Through guided projects and collaborative workshops, participants will navigate through a spectrum of robotics problems, ranging from navigation and obstacle avoidance to sensor calibration and autonomous decision-making. Along with the hands on robotics projects we will also be diving deep into the fundamentals of kinematics, dynamics, control, and planning. To conclude the course, we will see how modern ideas in optimization and AI can enable autonomous mobile robots. This is an introductory class, and so our focus will be on understanding foundational robotic concepts while broadly looking at their application in the real world.

What is expected of you?

This class is aimed towards advanced undergraduates and particularly towards students who want to learn robotics and robot AI, but who have never worked on a robot before. This will be a HW and project driven class. There will be no mid-term or final exams. Instead, you will be expected to implement concepts discussed and taught in this class. A portion of the lecture slots will be devoted to hands-on tutorials taught by the course teaching assistant (please see the syllabus for details).

Lerrel Pinto

Instructor

Aadhithya Iyer

Teaching Assistant

Venkatesh Pattabiraman

Teaching Assistant

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Logistics

Put the contents of the course here.

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Prerequisites

  • Linear algebra, basics of probability, solving basic differential equations
  • Coding in Python and familiarity with Ubuntu OS.

Logistical Overview

Lecture

  • 3.30pm-4.45pm on Tuesdays and Thursdays at 60FA C15.
  • Lectures will be held twice a week in person.
  • The google calendar may be accessed here.
  • The class will not be recorded.

Course Staff & Office Hours

  • Instructor: Lerrel Pinto

    • Office Hours: Tue 5:00-6:00pm, @ 60 Fifth Ave, 505

  • Teaching Assistants: Aadhithya Iyer & Venkatesh Pattabiraman

    • Office Hours: Friday 2:00-3:00pm, @ 60 Fifth Ave, 540

Grading

  1. Homeworks and projects (90%)
  2. Discussions, both in class and in campuswire (10%)

Assignments

  • Assignments will use Python 3 and RaspberryPI 4. We will provide you a virtual environment to install dependencies.

Remarks

  • A student in this course is expected to act professionally. Please also follow the GSAS regulations on academic integrity found here: http://gsas.nyu.edu/page/academic.integrity
  • Academic accommodations are available for students with disabilities. Please contact the Moses Center for Students with Disabilities (212-998-4980 or mosescsd@nyu.edu) for further information. Students who are requesting academic accommodations are advised to reach out to the Moses Center as early as possible in the semester for assistance.
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Markdown page example

You don't need React to write simple standalone pages.

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Syllabus

Put the contents of the course here.

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-

Course Content

DateLectures and HomeworksHW scheduleRelated Links
Part 1: Introduction to Robotics Control
9/5/2023Class 1: Introduction to the course
  • What is a Robot?
  • The anatomy of a robot
  • Role of intelligence in robotics
HW 1 released1. What is a Robot?
2. Anatomy of a Robot.
3. Isaac Asimov's Robot Series Other useful links:
1. Learn Git
2. Linux Terminal Game
3. Learn Shell
4. Linux Journey
9/07/2023Class 2: Optimization 101A quick intro to SGD and its variants
09/12/2023Tutorial1: Mechatronics 101
09/14/2023Class 3: Gradient DescentReference
09/19/2023Tutorial 2: Mechatronics - II
09/21/2023Class 4: Supervised LearningHW 1 dueChapter 2 of MLS textbook
09/26/2023Class 5: Rigid Body Transformations IHW 2 releasedChapter 2 of MLS textbook
09/28/2023Class 6: Rigid Body Transformations IIChapter 2 of MLS textbook
10/03/2023Class 7: Forward Kinematics1. Chapter 3 of MLS textbook
2. Forward kinematics of a variety of robots.
3. CH3 of Forward Kinematics
10/05/2023Class 8: Inverse KinematicsHW 2 due1. Chapter 3 of MLS textbook
2. Chapter 6 of Robotic Systems
10/12/2023Class 9: Robot Dynamics & JacobianHW 3 released1. Chapter 3 of MLS textbook
2. Section II of Robotic Systems
10/17/2023Tutorial3: Coding Transforms
10/19/2023Tutorial4: Coding Jacobians
10/24/2023Guest Lecture by Raunaq Bhirangi
10/26/2023Class 10: Introduction to ControlHW 3 due1. Chapter 4 of MLS textbook
2. Section IV of Robotic Systems
10/31/2023Class 11: Linear Quadratic RegulatorsHW 4 releasedVideo on Micro Mouse
11/02/2023Class 12: Non-Linear Dynamics and LQR variantsFormulation of LQR
11/07/2023Tutorial5: Coding LQRs
Part 2: Sensing and Fusion
11/09/2023Class 13: Robot SensingBayes rule wiki
11/14/2023Class 14: Introduction to filtering: Kalman Filters, Extended Kalman Filters.HW 4 dueKF tutorial
11/16/2023Tutorial6: Coding Bayes Rule.HW 5 released
Part 3: Planning
11/21/2023Class 15: Simultaneous Localization and MappingSimultaneous Localization and Mapping.
11/28/2023Class 16: Planning and configuration spaces.
11/30/2023Class 16: Heuristic planning and Sample Based PlannersHW 5 due
Part 4: Advanced Topics
12/05/2023Class 17: Computer Vision for RoboticsHW 6 released
12/07/2023Class 18: Model Predictive Control.
12/12/2023Class 19: Imitation Learning
12/14/2023Class 20: Reinforcement LearningHW 6 due

  1. MIT OCW Introduction to Robotics

  2. UW Mobile Robots

  3. MLS textbook

  4. Kris Hauser's Robotic Systems draft

- +

Course Content

DateLectures and HomeworksHW scheduleRelated Links
Part 1: Introduction to Robotics Control
9/5/2023Class 1: Introduction to the course
  • What is a Robot?
  • The anatomy of a robot
  • Role of intelligence in robotics
HW 1 released1. What is a Robot?
2. Anatomy of a Robot.
3. Isaac Asimov's Robot Series Other useful links:
1. Learn Git
2. Linux Terminal Game
3. Learn Shell
4. Linux Journey
9/07/2023Class 2: Optimization 101A quick intro to SGD and its variants
09/12/2023Tutorial1: Mechatronics 101
09/14/2023Class 3: Gradient DescentReference
09/19/2023Tutorial 2: Mechatronics - II
09/21/2023Class 4: Supervised LearningHW 1 dueChapter 2 of MLS textbook
09/26/2023Class 5: Rigid Body Transformations IHW 2 releasedChapter 2 of MLS textbook
09/28/2023Class 6: Rigid Body Transformations IIChapter 2 of MLS textbook
10/03/2023Class 7: Forward Kinematics1. Chapter 3 of MLS textbook
2. Forward kinematics of a variety of robots.
3. CH3 of Forward Kinematics
10/05/2023Class 8: Inverse KinematicsHW 2 due1. Chapter 3 of MLS textbook
2. Chapter 6 of Robotic Systems
10/12/2023Class 9: Robot Dynamics & JacobianHW 3 released1. Chapter 3 of MLS textbook
2. Section II of Robotic Systems
10/17/2023Tutorial3: Coding Transforms
10/19/2023Tutorial4: Coding Jacobians
10/24/2023Guest Lecture by Raunaq Bhirangi
10/26/2023Class 10: Introduction to ControlHW 3 due1. Chapter 4 of MLS textbook
2. Section IV of Robotic Systems
10/31/2023Class 11: Linear Quadratic RegulatorsHW 4 releasedVideo on Micro Mouse
11/02/2023Class 12: Non-Linear Dynamics and LQR variantsFormulation of LQR
11/07/2023Tutorial5: Coding LQRs
Part 2: Sensing and Fusion
11/09/2023Class 13: Robot SensingBayes rule wiki
11/14/2023Class 14: Introduction to filtering: Kalman Filters, Extended Kalman Filters.HW 4 dueKF tutorial
11/16/2023Tutorial6: Guest Lecture from ViamHW 5 released
Part 3: Planning
11/21/2023Class 15: Simultaneous Localization and MappingSimultaneous Localization and Mapping.
11/28/2023Class 16: Planning and configuration spaces.
11/30/2023Class 16: Heuristic planning and Sample Based PlannersHW 5 due
Part 4: Advanced Topics
12/05/2023Class 17: Computer Vision for RoboticsHW 6 released
12/07/2023Class 18: Model Predictive Control.
12/12/2023Class 19: Imitation Learning
12/14/2023Class 20: Reinforcement LearningHW 6 due

  1. MIT OCW Introduction to Robotics

  2. UW Mobile Robots

  3. MLS textbook

  4. Kris Hauser's Robotic Systems draft

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