awesome-list

Awesome List of Controls, Vision, Planning

Interdisciplinary

Repositories from Industries

1. Google
2. Meta
3. Snap
4. NASA
5. Deepmind
6. Qualcomm
7. Microsoft
8. Intel
9. Toyota Research Institute
10. Magic Leap
11. Neural Magic
12. Tangram Vision

Repositories from Academia

1. Robotic Systems Lab
2. Autonomous Systems Lab, torchfilter
3. Air Lab
4. xLab for Safe Autonomous Systems

Research

1. https://github.com/MLNLP-World/Top-AI-Conferences-Paper-with-Code
2. AI Conference Deadlines
3. papers.labml.ai)
4. AAAI
5. https://github.com/Hippogriff/ML-Resources/blob/master/courses.org
6. MATH-GA 2821 Optimization-based Data Analysis

People

1. Tom Goldstein
2. Haizhao Yang
3. Ming C. Lin
4. Maria K. Cameron - Math

Companies

1. Microsoft Research

Vision

1. Google Scholar: Top Vision conferences
2. List of Conferences and Deadline
3. CVF, CVF Open Access, Computer Vision Awards
4. https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
5. https://cmsc733.github.io/2019/proj/p3/
6. https://github.com/anirudhtopiwala/ENPM-673-Perception-for-Autonomous-Robots
7. https://github.com/SilenceOverflow/Awesome-SLAM
8. https://github.com/thien94/Another_VO_SLAM_List
9. https://github.com/dectrfov/awesome_3DReconstruction_list
10. Objectron
11. Shape for shading: https://github.com/hongzimao/shapeFromShading
12. Shape Manifolds lecture slides - basics
13. Classical Vision - Books 💥
14. 3D Machine Learning, Holistic 3D Reconstruction

People

1. https://mengzephyr.com/
2. https://www.mmlab-ntu.com/person/ccloy/publication_topic.html

Institutes

1. MIT 6.801/6.866 Machine Vision, 2004
2. MIT 6.819/6.869: Advances in Computer Vision. Example Repository
3. UT Austin - GAMES Advanced Course 3D Reconstruction and Understanding
4. CMU 16-721 Learning-Based Methods in Vision
5. Stanford CS231n Convolutional Neural Networks for Visual Recognition
6. Stanford EE367 / CS448I: Computational Imaging
7. Stanford Convex Optimization Short Course, Total Variation in-Painting
8. MIT 6.838: Shape Analysis (Spring 2021)
9. Ideas, Problem statements from EPFL. 💯
10. Oxford Active Vision Laboratory - code

Companies

1. https://research.adobe.com/publications/

Controls

1. Google Scholar: Top Conrols conferences
2. The Impact of Control Technology - 2nd edition

People

1. S. Shankar Sastry
2. Pieter Abbeel
3. Sanjay Lall
4. Boyd
5. Krishna Prasad
6. Miroslav Krstic

Institutes

1. MIT 6.800/6.843 Robotic Manipulation
2. MIT 6.832: Underactuated Robotics. Further Materials
3. MIT 16.332 Formal Methods for Safe Autonomous Systems
4. MIT 16.338[J] Dynamic Systems and Control
5. MIT 16.31/16.30 Feedback Control Systems
6. MIT 16.32 Principles of Optimal Control and Estimation
7. MIT 16.343 Spacecraft and Aircraft Sensors and Instrumentation
8. MIT 16.346 Astrodynamics
9. Stanford EE263: Introduction to Linear Dynamical Systems -> great slides on math background.
10. UMD ENEE 769R - Advanced Topics in Control, Principles and Algorithms for Collectives: from Biology to Robotics
11. UMD ENEE 660 - System Theory
12. UMD ENEE 620: Random Processes in Communications and Control
13. UMD CMSC 764 | ADVANCED NUMERICAL OPTIMIZATION

Stohastic Control

1. Stanford EE365: Stochastic Control

Non-Linear

1. UMD ENEE 661 - Nonlinear Control Systems

Optimal

1. https://github.com/ToniRV/MIT-16.32-Autonomous-Drone-Racing

Adaptive

1. https://github.com/aleixpb2/2.153-adaptive-controller-quadrotor
2. UMD ENEE 765 - Adaptive Control (and Learning Theory)

Swarm

1. https://github.com/yangliu28/swarm_formation_sim
2. https://github.com/yxiao1996/SwarmSim
3. https://github.com/gnotomista/swarm_sim
4. https://github.com/eshimelis/info_control

Other links:

1. https://github.com/SergeiSa/Control-Theory-Slides-Spring-2021

Planning

1. Google Scholar: Top Game theory/Decision science conferences

Institutes

1. MIT 6.413[J]/6.877[J] Principles of Autonomy and Decision Making
2. MIT 16.410[J]/6.817[J] Principles of Autonomy and Decision Making
3. MIT 16.420 Planning Under Uncertainty
4. MIT 16.485 Visual Navigation for Autonomous Vehicles

Conferences

1. https://www.icaps-conference.org/

Learning

1. CMSC 828W: Foundations of Deep Learning
2. CS 6789: Foundations of Reinforcement Learning 💥

Robotics

1. Google Scholar: Top Robotics conferences
2. Arxiv CS:Robotics
3. Google Brain Robotics team
4. Nikolay Atanasov
5. https://github.com/jslee02/awesome-robotics-libraries
6. https://github.com/mathworks-robotics/awesome-matlab-robotics
7. MIT 6.808[J] Mobile and Sensor Computing
8. https://github.com/dectrfov/ICRA2021PaperList

People

1. https://www.mit.edu/~arosinol/
2. Daniela Rus

Institutes

1. https://www.iris.ethz.ch/
2. https://rsl.ethz.ch/research/researchtopics.html

Companies

1. https://deepmind.com/research
2. https://www.merl.com/research/

Self-Driving cars

1. NuTonomy
2. Waymo
3. Lyft
4. University of Tübingen Lecture: Self-Driving Cars

Embedded

1. MIT 6.846 Parallel Computing
2. MIT 6.816/6.836 Multicore Programming()
3. MIT 6.827 Algorithm Engineering
4. MIT 6.850 Geometric Computing
5. MIT 16.35 Real-Time Systems and Software
6. MIT 6.823 Computer System Architecture
7. MIT 6.818 Dynamic Computer Language Engineering
8. MIT 18.337J/6.338J: Parallel Computing and Scientific Machine Learning. Collection ⭐
9. UMD ENEE 447 - Operating Systems by B. Jacob

GPU

1. Nvidia - CUDA Samples
2. CUDA C++ Programming Guide
3. CMake CUDA-ToolKit
4. https://rapids.ai/
5. An introduction to GPU computing - Slides

Tools

1. Connected Papers

Software Engineering Practices

1. Design Patterns
2. CPPCon

Stuffs:

1. Gang of Four Design Patterns
2. Cornell Reuleaux Kinematic Mechanisms Collection