Resources

In my journey of exploring and learning topics like robotics, optimization, control, and deep learning, I have encountered numerous invaluable resources. These include insightful YouTube videos and comprehensive blog posts that I have discovered and benefited from.

To share these execellent learning materials with others who are on a similar path, I have curated a list of these resources organized by topic as shown below.

Robotics

• Robotics Simulator

  MuJoCo developed by Google DeepMind

  • MuJoCo Bootcamp that programming in both C++ and Python by Prof. Pranav Bhounsule.

  Isaac Lab and Isaac Sim developed by Nvidia

  • Official Tutorial [Self-Paced Course] and many others by Nvidia
  • Issac Sim Tutorial (in Korean) and Issac Gym Tutorial (in Korean)

• Roboitc Manipulation and Legged Robots

  • Robotic Manipulation and Underactuated Robotics by Prof. Russ Tedrake

  • pinocchio, pyroboplan ( documentation)

  • Robot Manipulator Control and Contact-Aware Control

  • Legged Robotics by Prof. Pranav Bhounsule.

• Algorithms Related

  • 106B/206B Discussion at UC Berkeley

  • AI and Machine Learning for Robots by Prof. Wenhua Yu

Control

• Optimal Control (CMU 16-745) by Prof. Zac Manchester.

• Digital Control
  • Discrete Control by Brian Douglas
  • ELEC 3004 Digital Linear Systems: Signals & Control (University of Queesland)
  • ECE 4540 Digital Control Systems (University of Colorado)
• Adaptive Control
  • System Identification and Adaptive Controls by Prof Xu Chen
  • Adaptive Control and Intersections with Reinforcement Learning from Princeton (seminar)
• Reinforcement Learning
  • Reinforcement Learning: Machine Learning Meets Control Theory by Prof. Steve Brunton
  • CS 182 Lecture 15-16 by Prof. Sergey Levine
  • 深度强化学习基础

Optimization

• Intro to Optimization by Prof. Lewis Mitchell
• Approximate Dynamic Programming by Prof. Dimitri P. Bertsekas
• Automatic Differentiation, Adjoints & Sensitivities
• Deriving the Adjoint Equation for Neural ODEs using Lagrange Multipliers

Deep Learning

• Neural Networks Architecture
  • An introduction to RNN and LSTM
  • Illustrated Guide to LSTM’s and GRU’s: A step by step explanation

• Physics Informed Machine Learning by Prof. Steve Brunton

• Natural Language Processing and Large Language Models by Serrano.Academy