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Resources

LaTeX Template [Overleaf] [Download]

Assignments

Homework 1

Homework 2

Homework 3

Homework 4

Homework 5

Homework 6

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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 b4f51cda..496c1474 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 2024

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 homework 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

Jeff Cui

Teaching Assistant

Mohit Warke

Teaching Assistant

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Logistics

Put the contents of the course here.

- + \ No newline at end of file diff --git a/logistics/index.html b/logistics/index.html index 6533b94c..059bc3b0 100644 --- a/logistics/index.html +++ b/logistics/index.html @@ -6,13 +6,13 @@ Logistics | Introduction to Robot Intelligence - +

Prerequisites

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

Logistical Overview

Lecture

  • 3:30pm - 4:45pm, Tuesdays and Thursdays.
  • The first couple of lectures will be on Zoom so that everyone can participate (including those on the waitlist).
  • Lectures are in person at 60 Fifth Avenue (Center for Data Science), Room C15. To get there, enter the building and go down the stairs to your right.
  • Class calendar: Google Calendar.

Course Staff & Office Hours

  • Instructor: Lerrel Pinto
    • Instructor office hours are available upon request.
  • Teaching Assistants: Jeff Cui, Mohit Warke
    • Office Hours: Friday 1:00 - 2:00pm, @ 60 Fifth Avenue, 502

Grading

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

Assignments

  • See Assignments page for details.
  • There will be problem sets with theory and coding portions, and team projects on a rover robot.
  • We will use Python for all assignments. For the robot assignments, you will be running your code on a Raspberry Pi 4B onboard the rover.

Remarks

  • A student in this course is expected to act professionally. Please also follow the NYU regulations on academic integrity found here: https://cas.nyu.edu/academic-integrity.html
  • 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

DateLecturesHomeworks
Part 1: Introduction to Robotics Control
09/03/2024 (Tue)Class 1: Introduction to the courseHW 1 Released
09/05/2024 (Thu)Tutorial 1: numpy/scipy, rover setup Colab
09/10/2024 (Tue)Class 2: Optimization 101
09/12/2024 (Thu)Class 3: Gradient Descent
09/17/2024 (Tue)Tutorial 2: Mechatronics - II
09/19/2024 (Thu)Class 4: Supervised LearningHW 1 Due
09/24/2024 (Tue)Class 5: Rigid Body Transformations IHW 2 Released
09/26/2024 (Thu)Class 6: Rigid Body Transformations II
10/01/2024 (Tue)Class 7: Forward Kinematics
10/03/2024 (Thu)Class 8: Inverse KinematicsHW 2 Due
10/08/2024 (Tue)Class 9: Robot Dynamics & JacobianHW 3 Released
10/10/2024 (Thu)Tutorial 3: Coding Transforms
10/15/2024 (Tue)Tutorial 4: Coding Jacobians
10/17/2024 (Thu)Guest Lecture: TBD
10/22/2024 (Tue)Class 10: Introduction to ControlHW 3 Due
10/24/2024 (Thu)Class 11: Linear Quadratic RegulatorsHW 4 Released
10/29/2024 (Tue)Class 12: Non-Linear Dynamics and LQR Variants
Part 2: Sensing and Fusion
10/31/2024 (Thu)Class 13: Robot Sensing
11/05/2024 (Tue)Tutorial 5: Coding LQRs
11/07/2024 (Thu)Class 14: Introduction to filtering: Kalman Filters, Extended Kalman FiltersHW 4 Due
11/12/2024 (Tue)Guest Lecture: TBD
Part 3: Planning
11/14/2024 (Thu)Class 15: Simultaneous Localization and MappingHW 5 Released
11/19/2024 (Tue)Class 16: Planning and Configuration SpacesHW 6 Released
11/21/2024 (Thu)Class 17: Heuristic Planning and Sample-Based Planners
Part 4: Advanced Topics
11/26/2024 (Tue)Class 18: Computer Vision for RoboticsHW 5 Due
12/03/2024 (Tue)Class 19: Model Predictive Control
12/05/2024 (Thu)Class 20: Imitation Learning
12/10/2024 (Tue)Class 21: Reinforcement LearningHW 6 Due
12/12/2024 (Thu)TBD
  1. MIT OCW Introduction to Robotics
  2. UW Mobile Robots
  3. MLS textbook
  4. Kris Hauser's Robotic Systems draft
  5. Linux shell and toolchain: The Missing Semester of Your CS Education
- +

Course Content

DateLecturesHomeworks
Part 1: Introduction to Robotics Control
09/03/2024 (Tue)Class 1: Introduction to the courseHW 1 Released
09/05/2024 (Thu)Tutorial 1: numpy/scipy, rover setup [Colab] [Colab with solutions]
09/10/2024 (Tue)Class 2: Optimization 101
09/12/2024 (Thu)Class 3: Gradient Descent
09/17/2024 (Tue)Tutorial 2: Mechatronics - II
09/19/2024 (Thu)Class 4: Supervised LearningHW 1 Due
09/24/2024 (Tue)Class 5: Rigid Body Transformations IHW 2 Released
09/26/2024 (Thu)Class 6: Rigid Body Transformations II
10/01/2024 (Tue)Class 7: Forward Kinematics
10/03/2024 (Thu)Class 8: Inverse KinematicsHW 2 Due
10/08/2024 (Tue)Class 9: Robot Dynamics & JacobianHW 3 Released
10/10/2024 (Thu)Tutorial 3: Coding Transforms
10/15/2024 (Tue)Tutorial 4: Coding Jacobians
10/17/2024 (Thu)Guest Lecture: TBD
10/22/2024 (Tue)Class 10: Introduction to ControlHW 3 Due
10/24/2024 (Thu)Class 11: Linear Quadratic RegulatorsHW 4 Released
10/29/2024 (Tue)Class 12: Non-Linear Dynamics and LQR Variants
Part 2: Sensing and Fusion
10/31/2024 (Thu)Class 13: Robot Sensing
11/05/2024 (Tue)Tutorial 5: Coding LQRs
11/07/2024 (Thu)Class 14: Introduction to filtering: Kalman Filters, Extended Kalman FiltersHW 4 Due
11/12/2024 (Tue)Guest Lecture: TBD
Part 3: Planning
11/14/2024 (Thu)Class 15: Simultaneous Localization and MappingHW 5 Released
11/19/2024 (Tue)Class 16: Planning and Configuration SpacesHW 6 Released
11/21/2024 (Thu)Class 17: Heuristic Planning and Sample-Based Planners
Part 4: Advanced Topics
11/26/2024 (Tue)Class 18: Computer Vision for RoboticsHW 5 Due
12/03/2024 (Tue)Class 19: Model Predictive Control
12/05/2024 (Thu)Class 20: Imitation Learning
12/10/2024 (Tue)Class 21: Reinforcement LearningHW 6 Due
12/12/2024 (Thu)TBD
  1. MIT OCW Introduction to Robotics
  2. UW Mobile Robots
  3. MLS textbook
  4. Kris Hauser's Robotic Systems draft
  5. Linux shell and toolchain: The Missing Semester of Your CS Education
+ \ No newline at end of file