Some records of arXiv papers, GRASP seminars, and resources during my PhD period.
- 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
- CDC 2023 Workshop on Benchmarking, Reproducibility, and Open-Source Code in Controls
- IROS 2023 Workshop on Leveraging Models for Contact-Rich Manipulation
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.
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CSC2621 Topics in RoboticsReinforcement Learning in Robotics, UToronto
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CIS 5150: Linear Algebra for Computer Vision, Robotics, and Machine Learning, Upenn
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CIS 610, Spring 2023 Advanced Geometric Methods in Computer Science, Upenn
These are the robotic labs I pay special attention to.
- 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
- The Art of Linear Algebra
- Autonomous Racing Literature
- PhD Bibliography on Optimal Control, Reinforcement Learning and Motion Planning
- Deep Implicit Layers
- 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
- 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
- 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
- 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
- Science Plots
- rosbag_fancy
- Manim, designed for creating explanatory math videos.
- Quick C++ Benchmark
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- Use: automatic control and dynamic optimization. It can solve MPC, but has some limits
- License: open source
- Interface: C++, with MATLAB
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- Use: nonlinear optimization and algorithmic differentiation
- License: open source
- Interface: C++, Python or Matlab/Octave
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- 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
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- Use: non-linear Least Squares with bounds constraints/ unconstrained optimization
- License: open source
- Interface: C++ library
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- Use: linear/quadratic/semidefinite solver
- License: open source
- Interface: Matlab/Octave
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- Use: non-linear numerical optimization
- License: open source
- Interface: C++ library
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- Use: large scale sparse linear programming
- License: open source
- Interface: C, C#, FORTRAN, Julia and Python
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- Use: Google Optimization Tools
- License: open source
- Interface: C++, but also provide wrappers in Python, C# and Java
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- Use: IBM optimization studio
- License: have Free Edition
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- 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
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- Use: some types of optimizations. conic, QP, SDP...
- License: Academic Licenses
- Interface: C++, C, python, Matlab
- Tutorials: https://github.com/MOSEK/Tutorials
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- Use: QP
- License: MA27 from the HSL Archive
- Interface: object-oriented C++ package
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- Use: LP, QP and MIP (MILP, MIQP, and MIQCP)
- License: Academic Licenses
- Interface: C++, C, Python, Matlab, R...
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- Use: for convex second-order cone programs (SOCPs)
- License: open source
- Interface: C, Python, Julia, R, Matlab