A curated collection of papers, seminars, courses, and technical resources for robotic research.
- RSS 2025 Leveraging Implicit Methods for Aerial Autonomy
- ICRA 2025 25 years of Aerial Robotics: Challenges and Opportunities
- ICRA 2025 Novel Approaches for Precision Agriculture and Forestry with Autonomous Robots
- 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
- ICRA 2025 Workshop on Field Robotics
- ICRA 2025 Workshop on Public Trust in Autonomous Systems (PTAS)
- ICRA 2025 Towards Agility and Robustness: Mechanical Intelligence in Robotics, Biology, and Smart Materials
- CDC 2023 Workshop on Benchmarking, Reproducibility, and Open-Source Code in Controls
- IROS 2023 Workshop on Leveraging Models for Contact-Rich Manipulation
- IEEE International Conference on Robotics and Automation (ICRA)
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Robotics: Science and Systems (RSS)
- International Symposium of Robotics Research
- International Symposium of Experimental Robotics
- Workshop on the Algorithmic Foundations of Robotics
- Conference on Nonlinear Model Predictive Control
- ROSCon
- Roboranking: A Robotics Faculty Hub and University Ranking System
- A.I. Author Rankings by Publications
- A Normalized Professor Placement Guide to CS PhD Rankings
- CS 4756/5756 Robot Learning, Cornell University
- CSCI 699: Robot Learning, USC
- CSC2621 Topics in Robotics Reinforcement Learning in Robotics, UToronto
- CS 285 Deep Reinforcement Learning at UC Berkeley
- CS231n: Deep Learning for Computer Vision
- ESE 546: Principles of Deep Learning, UPenn
- ESE 650 Learning in Robotics, UPenn
- CIS 7000: Large Language Models, UPenn
- Introduction to Robot Learning, CMU 16-831
- Robot Learning, Yale University
- CS234: Reinforcement Learning, Stanford
- 16-350 Planning Techniques for Robotics, CMU
- MIT 6.832: Underactuated Robotics
- MIT 16.412J Cognitive Robotics, Spring 2016
- Stanford EE364a: Convex Optimization I
- CIS 5150: Linear Algebra for Computer Vision, Robotics, and Machine Learning, Upenn
- MA 430 Differential Geometry
- CIS 610, Spring 2023 Advanced Geometric Methods in Computer Science, Upenn
- COS597C: Advanced Methods in Probabilistic Modeling, Princeton
- Kumar Robotics Lab, UPenn
- FAST Lab, Zhejiang University
- MIT-ACL (Aerospace Controls Laboratory)
- TU Delft-Autonomous Multi-Robots Laboratory
- HKUST-Aerial Robotics Group
- UZH-RPG (Robotics and Perception Group)
- UCSD-ERL (Engineering Robotics Lab, UCSD)
- SNU-LARR (Laboratory for Autonomous Robotics Research)
- UCB-HiPeR Lab (High Performance Robotics Lab, UC Berkeley)
- AirLab, Carnegie Mellon University
- CMU-Robotic Exploration Lab
- Aerial Robotics Lab, Imperial College London
- MIT-LIDS (Laboratory for Information and Decision Systems)
- MIT-RLG (Robot Learning Group, MIT CSAIL)
- MIT-LIS (Learning and Intelligent Systems Group, MIT CSAIL)
- MIT-COCOSCI (Computational Cognitive Science Group)
1. General
- A Roadmap for US Robotics (2024 edition)
- Probabilistic Robotics
- Small Unmanned Aircraft: Theory and Practice
- Robotic Systems by Prof.Kris Hauser (University of Illinois at Urbana-Champaign)
- An Invitation to 3-D Vision From Images to Models
- The Extended Kalman Filter: An Interactive Tutorial for Non-Experts
- ACD
2. Math
-
Linear Algebra for Computer Vision, Robotics, and Machine Learning
-
Differential Geometry
1. General
- Convex Optimization
- A Course in Combinatorial Optimization
- CMU course, Convex Optimization
- Numerical Optimization
- Linear and Nonlinear Programming
- Proximal Algorithms
- Scaled form ADMM
- General Heuristics for Nonconvex Quadratically Constrained Quadratic Programming
- Nonlinear Optimization James V. Burke
- Optimization: Principles and Algorithms Michel Bierlair
2. Solvers
-
- Use: automatic control and dynamic optimization. It can solve MPC, but has some limits
- License: open source
- Interface: C++, with MATLAB
-
- Use: nonlinear optimization and algorithmic differentiation
- License: open source
- Interface: C++, Python or Matlab/Octave
-
- 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
-
- Use: non-linear Least Squares with bounds constraints/ unconstrained optimization
- License: open source
- Interface: C++ library
-
- Use: linear/quadratic/semidefinite solver
- License: open source
- Interface: Matlab/Octave
-
- Use: non-linear numerical optimization
- License: open source
- Interface: C++ library
-
- Use: large scale sparse linear programming
- License: open source
- Interface: C, C#, FORTRAN, Julia and Python
-
- Use: Google Optimization Tools
- License: open source
- Interface: C++, but also provide wrappers in Python, C# and Java
-
- Use: IBM optimization studio
- License: have Free Edition
-
- 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
-
- Use: some types of optimizations. conic, QP, SDP...
- License: Academic Licenses
- Interface: C++, C, python, Matlab
- Tutorials: https://github.com/MOSEK/Tutorials
-
- Use: QP
- License: MA27 from the HSL Archive
- Interface: object-oriented C++ package
-
- Use: LP, QP and MIP (MILP, MIQP, and MIQCP)
- License: Academic Licenses
- Interface: C++, C, Python, Matlab, R...
-
- Use: for convex second-order cone programs (SOCPs)
- License: open source
- Interface: C, Python, Julia, R, Matlab
1. General
- MIT Deep Learning
- Python Machine Learning (2nd edition)
- Awesome-Pytorch-list
- Deep Learning Models
- Awesome Deep Learning
- Awesome Public Datasets
- What’s the backward-forward FLOP ratio for Neural Networks?
- Machine Learning Collection
- Learn PyTorch for Deep Learning
- Step-by-Step Diffusion: An Elementary Tutorial
1. General
2. Open-source repo
- 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
- Hybrid A* Path Planner for the KTH Research Concept Vehicle
- 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
- Bilevel Planner
- DC3: A learning method for optimization with hard constraints
- GCOPTER
- smoothener: Convert multi-robot waypoint sequences into smooth piecewise polynomial trajectories.
- mav_trajectory_generation
- TOPP
- 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
- A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
- 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
- The Ten Most Important Rules of Writing Your Job Market Paper
- Doing a Systems PhD
- A Survival Guide to a PhD
- 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
- Lessons from My First 8 Years of Research
- The differences between tinkering and research
- How to Write Mathematics
- How to Write an Abstract
- How to Read a Paper
- How to Look for Ideas in Computer Science Research
- How to Have a Bad Career in Research/Academia
- How to manage your time as a researcher
- How to handle a hands-off supervisor
- How do I choose a principal investigator for my next postdoc?
- How to Build a Bad Research Center
- The Strategy Space
- Tips for Computer Science Faculty Applications
- AI for Grant Writing
- Academic job market: how to maximize your chances
- The Quick and Relatively Painless Guide to Your Academic Job Search
-
Website templates
- The Art of Linear Algebra
- Autonomous Racing Literature
- PhD Bibliography on Optimal Control, Reinforcement Learning and Motion Planning
- Deep Implicit Layers
- Inequality in Science: Who Becomes a Star?
- What Science and Nature are good for: causing paper cuts
- The Bitter Lesson
- Richard Hamming. "You and Your Research"
- We Are Sorry to Inform You …
- Poor Foundations in Geometric Algebra
- Academic mentees thrive in big groups, but survive in small groups
- How a PhD student’s lab size affects their chance of future academic success
- Postdocs’ lab engagement predicts trajectories of PhD students’ skill development
- Incorrect Baseline Evaluations Call into Question Recent LLM-RL Claims
- Respect the Unstable, Scale and Constraints in the Era of Artificial Intelligence
- Early coauthorship with top scientists predicts success in academic careers
- DeployableCoRL2023
- Neural network training makes beautiful fractals
- Why are some articles highly cited in applied linguistics? A bibliometric study
- Visual arXiv
- bibtex-tidy: Tidy bibtex files.
- Science Plots
- rosbag_fancy
- Manim, designed for creating explanatory math videos.
- Quick C++ Benchmark
- RL environment list
- OpenHands: Code Less, Make More
- Webots
- MuJoCo Physics
- Unreal Engine
- Simulation of Aerial Robotics
- ToddlerBot: Open-Source ML-Compatible Humanoid Platform for Loco-Manipulation
Drones
| Dataset | Data Types Included | Annotations/Labels Provided |
|---|---|---|
| Mid-Air | RGB images, Depth maps, Surface normals, Semantic labels, Stereo pairs, Camera poses, Sensor data (accelerometer, gyroscope) | Pixel-level semantic segmentation, Camera trajectory, Environmental conditions |
| Long-Range Drone Detection Dataset | Video footage or still images of airspace, Recordings at various distances and lighting | Bounding boxes of drones, Drone types, Distance labels, Detection confidence |
| Semantic Drone Dataset | High-res drone images of urban scenes, Pixel map for each image, Geo-tags | Pixel-level semantic segmentation (roads, buildings, humans, cars, vegetation, etc.) |
| DrIFT | Real & synthetic images, Aerial/ground PoVs, Multi-season/weather | Bounding boxes, Segmentation maps, Background class |
| M3ED | Stereo event camera data, Grayscale and RGB images, High-quality IMU, 64-beam LiDAR, RTK localization, Synchronized data from ground vehicles, legged robots, and aerial drones | Platform trajectory (RTK/odometry), Camera and sensor calibration, Labeling for egomotion, Event data timestamps |
| VisDrone | Images (static and video frames), Video sequences | Bounding boxes, Object class (car, bus, pedestrian, etc.), Occlusion levels, Truncation status, Object IDs (for tracking) |
| Pelican Dataset | Position data (x, y, z from Vicon), Orientation (roll, pitch, yaw), Actual motor speeds, Commanded motor speeds, Calculated velocities, Body rates (p, q, r), Stored as MATLAB cells per flight | Flight sample count, Full time-series measurements per field, Each flight treated as an individual data cell |