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.

Control

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

• Pontryagin meets Bellman from IFAC Seminar

• Adaptive Control
  • System Identification and Adaptive Controls by Prof. Xu Chen
  • Adaptive Control and Intersections with Reinforcement Learning from Princeton Seminar
• Data-Driven Control
  • Physics Informed Machine Learning by Prof. Steve Brunton
  • Sparsity and Compression & Data-Driven Dynamical Systems Overview by Prof. Steve Brunton
  • Data-Driven Modelling and Control with the Koopman Operator
• 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)
• Hybrid System Control
  • Feedback Control of Hybrid Dynamical Systems & Hybrid and Switched Control Systems
  • Model Predictive Control of Hybrid Dynamical Systems from Stanford Seminar
• Reinforcement Learning
  • Reinforcement Learning: Machine Learning Meets Control Theory by Prof. Steve Brunton
  • CS 182 Lecture 15-16 & CS 285 by Prof. Sergey Levine
  • Foundations of Deep RL Series by Prof. Pieter Abbeel

Deep Learning

• Deep Learning (An MIT Press book) & Towards Geometric Deep Learning

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

• Neural Networks Architecture
  • What are RNNs, how do they work? & An introduction to RNN and LSTM
  • Illustrated Guide to LSTM’s and GRU’s: A step by step explanation
  • MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention
• Generative Models
  • Variational Autoencoders - Generative AI Animated
  • What are Diffusion Models?

Differential Geometry

• Geometric tracking control of a quadrotor UAV on SE(3)

Optimization

• Intro to Optimization by Prof. Lewis Mitchell

• Approximate Dynamic Programming by Prof. Dimitri P. Bertsekas

• Ajoint Sensitivity Analysis
  • Automatic Differentiation, Adjoints & Sensitivities
  • Deriving the Adjoint Equation for Neural ODEs using Lagrange Multipliers
• Differentiable Programming
  • Introduction to Differentiable Programming
  • Machine Learning 10 - Differentiable Programming from Stanford CS221

Robotics

• Simulators and Differentiable Simulation

  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 and Issac Gym Tutorial (in Korean)

  Nvidia warp and mujoco_warp

  • Introduction, CoRL 2024 Workshop on Differentiable Optimization Everywhere

• Manipulation and Locomotion

  • 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

  • 106B/206B Discussion at UC Berkeley

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