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https://github.com/yuwei-wu/robophd

Some records and notes of weekly arXiv papers, GRASP seminars, and resources
https://github.com/yuwei-wu/robophd
motion-planning optimization robotics
Last synced: 9 days ago
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Some records and notes of weekly arXiv papers, GRASP seminars, and resources
Host: GitHub
URL: https://github.com/yuwei-wu/robophd
Owner: yuwei-wu
License: bsd-3-clause
Created: 2022-01-28T15:07:56.000Z (almost 3 years ago)
Default Branch: main
Last Pushed: 2024-12-04T02:13:08.000Z (about 1 month ago)
Last Synced: 2024-12-04T03:19:24.403Z (about 1 month ago)
Topics: motion-planning, optimization, robotics
Homepage:
Size: 250 KB
Stars: 137
Watchers: 9
Forks: 16
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

README

        # RoboPhD

Some records of arXiv papers, GRASP seminars, and resources during my PhD period.

## Workshops

### Planning and UAVs

- [ICRA 2024 Breaking Swarm Stereotypes](https://hauertlab.com/breaking-swarm-stereotypes-workshop-icra-2024/)

- [ICRA 2024 Agile Robotics: From Perception to Dynamic Action](https://agile-robotics-workshop.github.io/icra2024/)

- [ICRA 2024 Workshop on Field Robotics](https://norlab-ulaval.github.io/workshop_field_robotics_icra2024/)

- [ICRA 2023 MW07: Energy Efficient Aerial Robotic Systems](https://aerial-robotics-workshop-icra2023.com/)

- [ICRA 2023 FW30: Active methods in autonomous navigation](https://robotics.pme.duth.gr/workshop_active2/)

- [ICRA 2023 Bioinspired, Soft and Other Novel Design Paradigms for Aerial Robotics](https://aerial-robotics-workshop-icra2023.com/)

- [IROS 2023 Workshop on Integrated Perception, Planning, and Control for Physically and Contextually-Aware Robot Autonomy](https://ippc-iros23.github.io/)

### Others

- [CDC 2023 Workshop on Benchmarking, Reproducibility, and Open-Source Code in Controls](https://www.dynsyslab.org/cdc-2023-workshop-on-benchmarking-reproducibility-and-open-source-code-in-controls/)

- [IROS 2023 Workshop on Leveraging Models for Contact-Rich Manipulation](https://a2r-lab.org/courses/modelcontactiros23/)

## ArXiv Papers

**Updates: I will not frequently update ArXiv papers on this repo, as [Scholar Inbox](https://www.scholar-inbox.com/) is a much better tool for capturing papers!**

Also, check [robotics worldwide](https://www.robotics-worldwide.org/)

The papers and notes are updated weekly, mainly about motion planning.

- [2021](https://github.com/yuwei-wu/RoboPhD/blob/main/arXiv/2021.md)

- [2022-s1](https://github.com/yuwei-wu/RoboPhD/blob/main/arXiv/2022-s1.md)

- [2022-s2](https://github.com/yuwei-wu/RoboPhD/blob/main/arXiv/2022-s2.md)

- [2022-s3](https://github.com/yuwei-wu/RoboPhD/blob/main/arXiv/2022-s3.md)

- [2022-s4](https://github.com/yuwei-wu/RoboPhD/blob/main/arXiv/2022-s4.md)

- [2023-s1](https://github.com/yuwei-wu/RoboPhD/blob/main/arXiv/2023-s1.md)

- [2023-s2](https://github.com/yuwei-wu/RoboPhD/blob/main/arXiv/2023-s2.md)

## Courses

- [CS 4756/5756 Robot Learning, Cornell University](https://www.cs.cornell.edu/courses/cs4756/2023sp/)

- [CSCI 699: Robot Learning, USC](https://liralab.usc.edu/csci699/)

- [16-350 Planning Techniques for Robotics, CMU](https://www.cs.cmu.edu/~maxim/classes/robotplanning/)

- [CSC2621 Topics in RoboticsReinforcement Learning in Robotics, UToronto](https://www.pair.toronto.edu/csc2621-w20/)

- [CS 285 Deep Reinforcement Learning at UC Berkeley ](https://rail.eecs.berkeley.edu/deeprlcourse-fa22/)

- [CS231n: Deep Learning for Computer Vision](https://cs231n.stanford.edu/)

- [CIS 5150: Linear Algebra for Computer Vision, Robotics, and Machine Learning, Upenn](https://www.cis.upenn.edu/~cis5150/linalg-I-f.pdf)

- [ESE 546: Principles of Deep Learning, Upenn](https://pratikac.github.io/pub/23_ese546.pdf)

- [ESE 650 Learning in Robotics, Upenn](https://pratikac.github.io/pub/23_ese650.pdf)

- [CIS 7000: Large Language Models, Upenn](https://llm-class.github.io/schedule.html)

- [MA 430 Differential Geometry](https://liavas.net/courses/math430/)

- [CIS 610, Spring 2023 Advanced Geometric Methods in Computer Science, Upenn](https://www.cis.upenn.edu/~cis6100/)

- [COS597C: Advanced Methods in Probabilistic Modeling, Princeton](https://www.cs.princeton.edu/courses/archive/fall11/cos597C/)

- [More Robotics Courses](https://github.com/ajaygunalan/Robotics-Courses)

## Channels

### GRASP Seminars

- [2022](https://github.com/yuwei-wu/RoboPhD/blob/main/GRASP_Seminars/2022.md)

- [2023](https://github.com/yuwei-wu/RoboPhD/blob/main/GRASP_Seminars/2023.md)

### Others

- [DeployableCoRL2023](https://www.youtube.com/@DeployableCoRL2023/videos)

## Labs

These are the robotic labs I pay special attention to.

- [MIT-ACL](https://github.com/yuwei-wu/RoboPhD/blob/main/Labs/MIT-ACL.md)

- [MIT-LIDS](https://github.com/yuwei-wu/RoboPhD/blob/main/Labs/MIT-LIDS.md)

- [MIT-RLG](https://github.com/yuwei-wu/RoboPhD/blob/main/Labs/MIT-RLG.md)

- [MIT-LIS](https://lis.csail.mit.edu/)

- [MIT-COCOSCI](https://cocosci.mit.edu/)

  

- [ZJU-FAST Lab](https://github.com/yuwei-wu/RoboPhD/blob/main/Labs/ZJU-Fast.md)

- [Upenn-Kumar Lab](https://github.com/yuwei-wu/RoboPhD/blob/main/Labs/Upenn-Kumar.md)

- [TU Delft-Autonomous Multi-Robots Laboratory](https://github.com/yuwei-wu/RoboPhD/blob/main/Labs/TuDelft-Alonso-Mora.md)

- [HKUST-Aerial Robotics Group](https://github.com/yuwei-wu/RoboPhD/blob/main/Labs/Hkust-Shen.md)

- [UZH-RPG](https://github.com/yuwei-wu/RoboPhD/blob/main/Labs/UZH-RPG.md)

- [UCSD-ERL](https://github.com/yuwei-wu/RoboPhD/blob/main/Labs/UCSD-ERL.md)

- [SNU-LARR](https://github.com/yuwei-wu/RoboPhD/blob/main/Labs/SNU-LARR.md)

- [UCB-HiPeR Lab](https://github.com/yuwei-wu/RoboPhD/blob/main/Labs/UCB-HiPeR.md)

  

- [CMU-Air Lab](https://github.com/yuwei-wu/RoboPhD/blob/main/Labs/CMU-Air.md)

- [CMU-Robotic Exploration Lab](http://roboticexplorationlab.org/)

## Resources

- [Robotics](https://github.com/yuwei-wu/RoboPhD/blob/main/Resources/robo.md)

- [Optimization](https://github.com/yuwei-wu/RoboPhD/blob/main/Resources/optimization.md)

- [Learning](https://github.com/yuwei-wu/RoboPhD/blob/main/Resources/learning.md)

- [Computer Science](https://github.com/yuwei-wu/RoboPhD/blob/main/Resources/cs.md)

## Guidelines

- [Instructions to Ph.D. students by Prof.Dimitris Papadias](https://cse.hkust.edu.hk/~dimitris/Instructions%20for%20PhD%20Students.pdf)

- [Awesome tips for research](https://github.com/jbhuang0604/awesome-tips)

- [Ten simple rules for structuring papers](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005619)

- [Novelty in Science: A guide to reviewers](https://medium.com/@black_51980/novelty-in-science-8f1fd1a0a143)

- [The Ten Most Important Rules of Writing Your Job Market Paper](https://economics.harvard.edu/files/economics/files/tenruleswriting.pdf)

- [Doing a Systems PhD](https://www.cl.cam.ac.uk/research/srg/netos/eurosys11dw/keynote/StevenHand.pdf)

- [A Survival Guide to a PhD](https://karpathy.github.io/2016/09/07/phd/)

- [Maximize your research impact with storytelling](https://www.nature.com/articles/s41568-023-00616-z)

- [What advice would I give a starting graduate student interested in robot learning? Models! ... Model-free! ... Both!](https://www.cs.cmu.edu/~cga/mbl/)

- [Science Research Writing: For Non-Native Speakers of English](https://redacaocientifica.weebly.com/uploads/6/0/2/2/60226751/science_writing_for_non-native_engish_speakers.pdf)

- [Lessons from My First 8 Years of Research](https://tomsilver.github.io/blog/2024/lessons/)

- [The differences between tinkering and research](https://togelius.blogspot.com/2016/04/the-differences-between-tinkering-and.html)

### How to ...

- [How to Write Mathmatics](https://entropiesschool.sciencesconf.org/data/How_to_Write_Mathematics.pdf)

- [How to Write an Abstract](https://users.ece.cmu.edu/~koopman/essays/abstract.html)

- [How to Read a Paper](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf)

- [How to Look for Ideas in Computer Science Research](https://medium.com/digital-diplomacy/how-to-look-for-ideas-in-computer-science-research-7a3fa6f4696f)

- [How to Have a Bad Career in Research/Academia](https://people.eecs.berkeley.edu/~pattrsn/talks/BadCareer.pdf)

- [How to manage your time as a researcher](https://www.nature.com/articles/d41586-022-04364-2)

- [How to handle a hands-off supervisor](https://kidger.site/thoughts/how-to-handle-a-hands-off-supervisor/)

### Faculty Resources

- [The Strategy Space](https://www.kiragoldner.com/blog/index.html)

- [Tips for Computer Science Faculty Applications](https://yisongyue.medium.com/checklist-of-tips-for-computer-science-faculty-applications-9fd2480649cc)

- [A Normalized Professor Placement Guide to CS PhD Rankings](https://sethkarten.github.io/professor)

- [AI for Grant Writing](https://github.com/eseckel/ai-for-grant-writing)

### Templates

- [Review and Response Letters](https://github.com/mschroen/review_response_letter)

- [Best README](https://github.com/othneildrew/Best-README-Template)

- [Latex book](https://github.com/amberj/latex-book-template)

### Knowledge Notes

- [The Art of Linear Algebra](https://github.com/kenjihiranabe/The-Art-of-Linear-Algebra)

- [Autonomous Racing Literature](https://github.com/JohannesBetz/AutonomousRacing_Literature)

- [PhD Bibliography on Optimal Control, Reinforcement Learning and Motion Planning](https://github.com/eleurent/phd-bibliography)

- [Deep Implicit Layers](http://implicit-layers-tutorial.org/)

### More readings

- [A.I. Author Rankings by Publications](https://airankings.professor-x.de/)

- [Inequality in Science: Who Becomes a Star?](https://www.nber.org/papers/w33063)

- [What Science and Nature are good for: causing paper cuts](https://www.nature.com/articles/d41586-024-02297-6)

- [The Bitter Lesson](https://www.cs.utexas.edu/~eunsol/courses/data/bitter_lesson.pdf)

- [Richard Hamming. "You and Your Research"](https://www.cs.utexas.edu/~dsb/Inspiration/hamming.pdf)

- [We Are Sorry to Inform You …](https://www.cs.utexas.edu/~dsb/Inspiration/reject.pdf)

## Some Famous Planning/Control Repo

### (1) Planner

- [TEB Local Planner](https://github.com/rst-tu-dortmund/teb_local_planner)

- [Fast Planner](https://github.com/HKUST-Aerial-Robotics/Fast-Planner)

- [Teach-Repeat-Replan (Autonomous Drone Race)](https://github.com/HKUST-Aerial-Robotics/Teach-Repeat-Replan)

- [EGO-Planner-v2](https://github.com/ZJU-FAST-Lab/EGO-Planner-v2)

- [GPMP2](https://github.com/gtrll/gpmp2)

- [MRSL Motion Primitive Library](https://github.com/sikang/mpl_ros)

- [FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments](https://github.com/mit-acl/faster)

- [cmu-exploration](https://www.cmu-exploration.com/)

- [VAMP](https://github.com/KavrakiLab/vamp)

### (2) Multi-agent

- [multi-robot-trajectory-planning](https://github.com/whoenig/multi-robot-trajectory-planning)

- [Planner using Linear Safe Corridor](https://github.com/qwerty35/lsc_planner)

- [MADER: Trajectory Planner in Multi-Agent and Dynamic Environments](https://github.com/mit-acl/mader)

- [EGO-Swarm](https://github.com/ZJU-FAST-Lab/ego-planner-swarm)

- [Downwash-Aware Trajectory Planning for Large Quadcopter Teams](https://github.com/jpreiss/smoothener)

  

### (3) MPC /iLQR

- [Model Predictive Contouring Controller (MPCC)](https://github.com/alexliniger/MPCC)

- [Data-Driven MPC for Quadrotors](https://github.com/uzh-rpg/data_driven_mpc)

- [Policy Search for Model Predictive Control with Application to Agile Drone Flight](https://github.com/uzh-rpg/high_mpc)

- [Model Predictive Control for Multi-MAV Collision Avoidance in Dynamic Environments](https://github.com/tud-amr/mrca-mav)

- [MPC for Quadrotors with extension to Perception-Aware MPC](https://github.com/uzh-rpg/rpg_mpc)

- [KR iLQR Optimizer](https://github.com/KumarRobotics/kr_ilqr_optimizer)

- [Online trajectory generation with distributed model predictive control for multi-robot motion planning](https://github.com/carlosluis/online_dmpc)

### (4) Back-end Optimization

- [Bilevel Planner](https://github.com/OxDuke/Bilevel-Planner)

- [DC3: A learning method for optimization with hard constraints](https://github.com/locuslab/DC3)

- [GCOPTER](https://github.com/ZJU-FAST-Lab/GCOPTER)

### (5) Map representation

- [Voxblox](https://github.com/ethz-asl/voxblox)

- [Grid Map](https://github.com/ANYbotics/grid_map)

- [OctoMap](https://github.com/OctoMap/octomap)

- [Convex Decomposition of Free Space](https://github.com/sikang/DecompROS)

### (6) Benchmarks

- [Avoidbench](https://github.com/tudelft/AvoidBench)

- [Evaluating Dynamic Environment Difficulty for Collision Avoidance Benchmarking](https://smoggy-p.github.io/Evaluating_Dynamic_Difficulty/)

- [Design and Evaluation of Motion Planners for Quadrotors](https://github.com/KumarRobotics/kr_mp_design)

- [kinodynamic-motion-planning-benchmark](https://github.com/IMRCLab/kinodynamic-motion-planning-benchmark)

- [Bench-MR: A Motion Planning Benchmark for Wheeled Mobile Robots](https://github.com/robot-motion/bench-mr)

- [Local Motion Planning Benchmark Suite](https://github.com/tud-amr/localPlannerBench)

### (7) Learning-based 

- [Fast Kinodynamic Planning on the Constraint Manifold 

with Deep Neural Networks](https://sites.google.com/view/constrained-neural-planning/)

- [k-diffusion](https://github.com/crowsonkb/k-diffusion)

## Robo Tools

### (0) General tools

- [Science Plots](https://github.com/garrettj403/SciencePlots)

- [rosbag_fancy](https://github.com/xqms/rosbag_fancy)

- [Manim, designed for creating explanatory math videos.](https://github.com/3b1b/manim)

- [Quick C++ Benchmark](https://quick-bench.com/)

### (1) Solvers:

- [ACADO Toolkit](https://github.com/acado/acado)

  - Use: automatic control and dynamic optimization. It can solve MPC, but has some limits

  - License: open source

  - Interface: C++, with MATLAB

- [CasADi](https://github.com/casadi/casadi)

  - Use: nonlinear optimization and algorithmic differentiation

  - License: open source

  - Interface: C++, Python or Matlab/Octave

- [FORCES PRO](https://www.embotech.com/products/forcespro/overview/)

  - Use: code generator for optimization solver, very useful to solve nonlinear MPC

  - License:  [Academic Licenses](https://www.embotech.com/products/forcespro/licensing/)

  - Interface: C++, Python or Matlab /Simulink interface

  - Some examples: https://github.com/embotech/forcesnlp-examples

  

- [Ceres Solver](https://github.com/ceres-solver/ceres-solver)

  - Use: non-linear Least Squares with bounds constraints/ unconstrained optimization 

  - License: open source

  - Interface: C++ library

  

- [SeDuMi](https://github.com/sqlp/sedumi)

  - Use: linear/quadratic/semidefinite solver

  - License: open source

  - Interface: Matlab/Octave

  

- [Ensmallen](https://github.com/mlpack/ensmallen)

  - Use: non-linear numerical optimization

  - License: open source

  - Interface: C++ library

  

- [HiGHS](https://github.com/ERGO-Code/HiGHS)

  - Use: large scale sparse linear programming

  - License: open source

  - Interface: C, C#, FORTRAN, Julia and Python

- [OR-Tools](https://github.com/google/or-tools)

  - Use: Google Optimization Tools

  - License: open source

  - Interface: C++, but also provide wrappers in Python, C# and Java

- [CPLEX](https://www.ibm.com/products/ilog-cplex-optimization-studio)

  - Use: IBM optimization studio

  - License: have Free Edition

- [Drake](https://drake.mit.edu/)

  - Use: robotic toolbox, can solve optimizations, systems modeling, and etc.

  - License: open source

  - Interface: C++, python

  - Some examples: https://github.com/RobotLocomotion/drake-external-examples

- [Crocoddyl](https://github.com/loco-3d/crocoddyl)

- [YALMIP](https://yalmip.github.io/)

- [Mosek](https://www.mosek.com/)

  - Use: some types of optimizations. conic, QP, SDP...

  - License: [Academic Licenses](https://www.mosek.com/products/academic-licenses/)

  - Interface: C++, C, python, Matlab

  - Tutorials: https://github.com/MOSEK/Tutorials

  

- [OOQP](https://pages.cs.wisc.edu/~swright/ooqp/)

  - Use: QP

  - License: [MA27 from the HSL Archive](https://www.hsl.rl.ac.uk/download/MA27/1.0.0/a/)

  - Interface: object-oriented C++ package

- [Operator Splitting QP Solver (OSQP)](https://github.com/osqp/osqp)

- [Gurobi](https://www.gurobi.com/products/gurobi-optimizer/)

  - Use: LP, QP and MIP (MILP, MIQP, and MIQCP) 

  - License: [Academic Licenses](https://www.gurobi.com/academia/academic-program-and-licenses/)

  - Interface: C++, C, Python, Matlab, R...

  

- [Embedded Conic Solver (ECOS)](https://github.com/embotech/ecos)

  - Use: for convex second-order cone programs (SOCPs) 

  - License: open source

  - Interface: C, Python, Julia, R, Matlab

  

### (2) Simulations: 

- [Webots](https://github.com/cyberbotics/webots)

- [MuJoCo Physics](https://github.com/deepmind/mujoco)

- [Unreal Engine](https://github.com/EpicGames/UnrealEngine)

## Robotics Companies:

- [Berkshire Grey](https://www.berkshiregrey.com/)

- [fly4future](https://fly4future.com/)