RoboPhD

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

Workshops

Planning and UAVs

• ICRA 2024 Breaking Swarm Stereotypes
• ICRA 2024 Agile Robotics: From Perception to Dynamic Action
• ICRA 2024 Workshop on Field Robotics
• ICRA 2023 MW07: Energy Efficient Aerial Robotic Systems
• ICRA 2023 FW30: Active methods in autonomous navigation
• ICRA 2023 Bioinspired, Soft and Other Novel Design Paradigms for Aerial Robotics
• IROS 2023 Workshop on Integrated Perception, Planning, and Control for Physically and Contextually-Aware Robot Autonomy

Others

• CDC 2023 Workshop on Benchmarking, Reproducibility, and Open-Source Code in Controls
• IROS 2023 Workshop on Leveraging Models for Contact-Rich Manipulation

ArXiv Papers

Updates: I will not frequently update ArXiv papers on this repo, as Scholar Inbox is a much better tool for capturing papers!

Also, check robotics worldwide

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

• 2021

• 2022-s1

• 2022-s2

• 2022-s3

• 2022-s4

• 2023-s1

• 2023-s2

Courses

• CS 4756/5756 Robot Learning, Cornell University

• CSCI 699: Robot Learning, USC

• 16-350 Planning Techniques for Robotics, CMU

• CSC2621 Topics in RoboticsReinforcement Learning in Robotics, UToronto

• CS 285 Deep Reinforcement Learning at UC Berkeley

• CS231n: Deep Learning for Computer Vision

• CIS 5150: Linear Algebra for Computer Vision, Robotics, and Machine Learning, Upenn

• ESE 546: Principles of Deep Learning, Upenn

• ESE 650 Learning in Robotics, Upenn

• CIS 7000: Large Language Models, Upenn

• MA 430 Differential Geometry

• CIS 610, Spring 2023 Advanced Geometric Methods in Computer Science, Upenn

• More Robotics Courses

Channels

GRASP Seminars

• 2022

• 2023

Others

• DeployableCoRL2023

Labs

These are the robotic labs I pay special attention to.

• MIT-ACL

• MIT-LIDS

• MIT-RLG

• MIT-LIS

• MIT-COCOSCI

• ZJU-FAST Lab

• Upenn-Kumar Lab

• TU Delft-Autonomous Multi-Robots Laboratory

• HKUST-Aerial Robotics Group

• UZH-RPG

• UCSD-ERL

• SNU-LARR

• UCB-HiPeR Lab

• CMU-Air Lab

• CMU-Robotic Exploration Lab

Resources

• Robotics

• Optimization

• Learning

• Computer Science

Guidelines

• Instructions to Ph.D. students by Prof.Dimitris Papadias
• Awesome tips for research
• Ten simple rules for structuring papers
• Novelty in Science: A guide to reviewers
• How to Write Mathmatics
• The Ten Most Important Rules of Writing Your Job Market Paper
• How to Write an Abstract
• How to Have a Bad Career in Research/Academia
• Doing a Systems PhD
• How to manage your time as a researcher
• A Normalized Professor Placement Guide to CS PhD Rankings
• A.I. Author Rankings by Publications
• Maximize your research impact with storytelling
• What advice would I give a starting graduate student interested in robot learning? Models! ... Model-free! ... Both!
• Science Research Writing: For Non-Native Speakers of English
• The Bitter Lesson

Faculty Application Resources

• The Strategy Space
• Tips for Computer Science Faculty Applications

Templates

• Review and Response Letters
• Best README
• Latex book

Knowledge Notes

• The Art of Linear Algebra
• Autonomous Racing Literature
• PhD Bibliography on Optimal Control, Reinforcement Learning and Motion Planning
• Deep Implicit Layers

Some Famous Planning/Control Repo

(1) Planner

• TEB Local Planner
• Fast Planner
• Teach-Repeat-Replan (Autonomous Drone Race)
• EGO-Planner-v2
• GPMP2
• MRSL Motion Primitive Library
• FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments
• cmu-exploration
• VAMP

(2) Multi-agent

• multi-robot-trajectory-planning
• Planner using Linear Safe Corridor
• MADER: Trajectory Planner in Multi-Agent and Dynamic Environments
• EGO-Swarm
• Downwash-Aware Trajectory Planning for Large Quadcopter Teams

(3) MPC /iLQR

• Model Predictive Contouring Controller (MPCC)
• Data-Driven MPC for Quadrotors
• Policy Search for Model Predictive Control with Application to Agile Drone Flight
• Model Predictive Control for Multi-MAV Collision Avoidance in Dynamic Environments
• MPC for Quadrotors with extension to Perception-Aware MPC
• KR iLQR Optimizer
• Online trajectory generation with distributed model predictive control for multi-robot motion planning

(4) Back-end Optimization

• Bilevel Planner
• DC3: A learning method for optimization with hard constraints
• GCOPTER

(5) Map representation

• Voxblox
• Grid Map
• OctoMap
• Convex Decomposition of Free Space

(6) Benchmarks

• Avoidbench
• Evaluating Dynamic Environment Difficulty for Collision Avoidance Benchmarking
• Design and Evaluation of Motion Planners for Quadrotors
• kinodynamic-motion-planning-benchmark
• Bench-MR: A Motion Planning Benchmark for Wheeled Mobile Robots
• Local Motion Planning Benchmark Suite

(7) Learning-based

• Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks
• k-diffusion

Robo Tools

(0) General tools

• Science Plots
• rosbag_fancy
• Manim, designed for creating explanatory math videos.
• Quick C++ Benchmark

(1) Solvers:

• ACADO Toolkit

  • Use: automatic control and dynamic optimization. It can solve MPC, but has some limits
  • License: open source
  • Interface: C++, with MATLAB

• CasADi

  • Use: nonlinear optimization and algorithmic differentiation
  • License: open source
  • Interface: C++, Python or Matlab/Octave

• FORCES PRO

  • Use: code generator for optimization solver, very useful to solve nonlinear MPC
  • License: Academic Licenses
  • Interface: C++, Python or Matlab /Simulink interface
  • Some examples: https://github.com/embotech/forcesnlp-examples

• Ceres Solver

  • Use: non-linear Least Squares with bounds constraints/ unconstrained optimization
  • License: open source
  • Interface: C++ library

• SeDuMi

  • Use: linear/quadratic/semidefinite solver
  • License: open source
  • Interface: Matlab/Octave

• Ensmallen

  • Use: non-linear numerical optimization
  • License: open source
  • Interface: C++ library

• HiGHS

  • Use: large scale sparse linear programming
  • License: open source
  • Interface: C, C#, FORTRAN, Julia and Python

• OR-Tools

  • Use: Google Optimization Tools
  • License: open source
  • Interface: C++, but also provide wrappers in Python, C# and Java

• CPLEX

  • Use: IBM optimization studio
  • License: have Free Edition

• Drake

  • 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

• YALMIP

• Mosek

  • Use: some types of optimizations. conic, QP, SDP...
  • License: Academic Licenses
  • Interface: C++, C, python, Matlab
  • Tutorials: https://github.com/MOSEK/Tutorials

• OOQP

  • Use: QP
  • License: MA27 from the HSL Archive
  • Interface: object-oriented C++ package

• Operator Splitting QP Solver (OSQP)

• Gurobi

  • Use: LP, QP and MIP (MILP, MIQP, and MIQCP)
  • License: Academic Licenses
  • Interface: C++, C, Python, Matlab, R...

• Embedded Conic Solver (ECOS)

  • Use: for convex second-order cone programs (SOCPs)
  • License: open source
  • Interface: C, Python, Julia, R, Matlab

(2) Simulations:

• Webots

• MuJoCo Physics

• Unreal Engine

Robotics Companies:

• Berkshire Grey
• fly4future