Resources

This page is a collection of references, papers, and hardware vendors, and links that I’ve found useful over the years.

Table of Contents

References

List of books, lectures, and blogs I’ve found useful. 

Robotics

Texts (mostly classic) that are specifically dedicated to robots.

Comprehensive
  • B. Siciliano, O. Khatib, Springer Handbook of Robotics
  • R. Tedrake, Underactuated Robotics [website][lecture]
  • P. Corke, Robotics, Vision and Control
Kinematics, Dynamics, and Control
  • R. Murray, Z. Li, S. Sastry (MLS), A Mathematical Introduction to Robotic Manipulation [pdf]
  • J. Craig, Introduction to Robotics [pdf]
  • K. Lynch, F. Park, Modern Robotics: Mechanics, Planning, and Control [pdf]
  • N. Hogan, Impedance Control: An Approach to Manipulation [pdf]
  • G. Avanzini, Spacecraft Attitude Dynamics and Control [pdf]
  • A. De Luca’s Notes on Robot Kinematics & Dynamics [website][website]
Motion Planning
  • H. Choset, K. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. Kavraki, S. Thrun, Principles of Robot Motion: Theory, Algorithms, and Implementation [pdf]
  • H. Choset’s lecture notes on robotic motion planning [pdf]
  • J.C. Latombe, Robot Motion Planning
  • S.M. Lavelle, Planning Algorithms [pdf]
Perception, State Estimation, Sensor Fusion
  • S. Thrun, W. Burgard, D. Fox, Probabilistic Robotics [pdf]
  • T.D. Barfoot, State Estimation for Robotics [pdf]
Robotic Manipulation
  • M. Mason, Mechanics of Robotic Manipulation [notes]
  • E. Rimon, J. Burdick, The Mechanics of Robot Grasping
  • R. Tedrake, Robotic Manipulation [website]
Human-Robot Interaction
  • J. Tani, Exploring Robotic Minds

Applied Math

The bread and butter of robotics. 

Linear Algebra

 
  • S. Axler, Linear Algebra Done Right [link]
Analysis
 
  • W. Rudin, Principles of Mathematical Analysis (a.k.a. Baby Rudin) [pdf]
  • W. Rudin, Real and Complex Analysis (a.k.a. Papa Rudin) [pdf]
  • W. Rudin, Functional Analysis (a.k.a. Grandpa Rudin)[pdf]
  • N.L. Carothers, Real Analysis [pdf]
  • W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Numerical Recipes [pdf]
  • A. Toomre’s lectures notes on numerical analysis [pdf]
Optimization
  • D.Bertsimas, J. Tsitsiklis, Introduction to Linear Optimization [pdf]
  • S. Boyd, L. Vandenberghe, Convex Optimization [pdf][lecture]
  • E. Hazan, Introduction to Online Convex Optimization [pdf]
  • J. Nocedal, S.J. Wright, Numerical Optimization [pdf]
  • G. Blekherman, P. Parrilo, R. Thomas, Semidefinite Optimization and Convex Algebraic Geometry [slides]
  • C.H. Papadimitriou, K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity [pdf]
Dynamical Systems
  • H. Broer, F. Takens, Dynamical Systems and Chaos [pdf]
  • R.J. Brown, A Modern Introduction to Dynamical Systems
  • H. Arbabi, Introuduction to Koopman operator theory of dynamical systems [pdf]
Differential Geometry
  • J.M.Lee, Introduction to Smooth Manifolds [pdf]
Probability and Inference
  • K. Border’s lecture notes on Intro to Probability [link]
  • D. Shah’s lecture notes on Algorithms for Inference [link]

Control Theory

For those who like the abstract.

Linear / Classical Control
 
  • K.J. Astrom, R.M. Murray, Feedback Systems [pdf]
  • J. Doyle, B. Francis, A. Tannenbaum, Feedback Control Theory [pdf]
  • E. Sontag, Mathematical Control Theory [pdf]
  • A. Megrestki, Multivariable Control Systems [pdf]
Nonlinear Control
  • Khalil, Nonlinear Control [pdf]
  • A. Isidori, Nonlinear Control Systems
  • J.J. Slotine, W. Li, Applied Nonlinear Control [pdf][lectures]
  • A.D. Ames, P. Tabuada, Nonlinear Dynamics and Control (Not published yet)
Optimization-based Control
  • R.M. Murray, Opmization-Based Control [pdf]
  • F. Borrelli, A. Bemporad, M. Morari, Predictive Control for Linear and Hybrid Systems [pdf]
  • S. Boyd, L. Vandenberghe, Convex Optimization [pdf][lecture]
  • D. Bertsekas, Dynamic Programming and Optimal Control [pdf]
Hybrid Systems
  • J. Lygeros, Lecture Notes on Hybrid Systems [pdf]
  • B. De Schutter, Lecture Notes on Modeling & Control of Hybrid Systems [website]
  • W.P.M.H Heemels, B.De Schutter, A. Bemporad, Equivalence of Hybrid Dynamical Models [pdf]
  • A. Bemporad’s Notes on Model Predictive Control (Hybrid Models for MPC) [pdf]

Machine Learning

The recent hype.

Supervised Learning
 
  • H.T. Lin, M. Magon-Ismail, Y. Abu-Mostafa, Learning from Data [lectures]
  • I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning [pdf]
Reinforcement Learning
  • R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction [pdf]
  • D.P. Bertsekas, Dynamic Programming and Optimal Control [pdf]
  • D.P. Bertsekas, J.N. Tsitsiklis, Neuro-Dynamic Programming
  • Berkeley Deep RL Bootcamp [website]
  • I. Alex’s blog post on Deep Reinforcement Learning doesn’t work yet [blog]
Unsupervised Learning
 
  • H.T. Lin, M. Magon-Ismail, Y. Abu-Mostafa, Learning from Data [lectures]
  • L. Weng’s blog post on Autoencoders [blog]
  • C. Doersch, Tutorial on Variational Autoencoders [pdf]
  • I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning [pdf]
  • I. Goodfellow’s tutorial on Generative Adversarial Networks [pdf]

Computer Science

Foundational stuff.

Automata and Formal Verification
 
  • M. Sipser, Introduction to the Theory of Computation [pdf]
  • C. Baier, J.P. Katoen, Principles of Model Checking [pdf]
Computer Engineering
 
  • Bryant, O`Hallaron, Computer Systems: A Programmer’s Perspective [pdf]
  • A. Silberschatz, P.B. Galvin, G. Gagne, Operating System Concepts [pdf]
  • M. Herlihy, N. Shavit, The Art of Multiprocessor Programming [pdf]
Algorithms
 
  • H. Cormen, C.E. Leiserson, R.L. Rivest, C. Stein, Introduction to Algorithms [pdf]
  • M. de Berg, O. Cheong, M. van Kreveld, M. Overmars, Computational Geometry [pdf]
  • W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Numerical Recipes [pdf]
Project Management
 
  • T. DeMarco, T. Lister, Waltzing with Bears: Managing Risk on Software Projects 
Languages
 
  • S. Lippman, J. Lajoie, B. Moo, C++ primer
  • B. Stroustrup, The C++ Programming Language [pdf]

Mechanics, Design, and Electronics

The fun stuff!

Statics & Dynamics
 
  • F. Beer, E.R. Johnston, D.F. Mazurek, Vector Mechanics for Engineers
  • A.P. French, Vibrations and Waves 
  • G.R. Fowles, G.L. Cassidy, Analytical Mechanics [pdf]
  • S.T. Thornton, J.B. Marion, Classical Dynamics of Particles and Systems [pdf]
Continuum Mechanics
 
  • F.M. White, Fluid Mechanics [pdf]
  • F. Beer, E.R. Johnston, J.T. DeWolf, D.F. Mazurek, Mechanics of Materials [pdf]
  • R. Hibbler, Mechanics of Materials [pdf]
  • B.C. Khoo, J. White, J. Peraire, A.T. Patera, Lectures Notes on Numerical Methods for Partial Differential Equations [pdf]
  • C. Johnson, Numerical Solution of Partial Differential Equations by the Finite Element Method
Machine Design & Electronics
 
  • R.G. Budynas, J.K. Nisbett, Shigley’s Mechanical Engineering Design [pdf]
  • J.E. Shigley, C.R. Mischke, Standard Handbook of Machine Design [pdf]
  • P. Horowitz, W. Hill, The Art of Electronics [pdf]
  • M. Zalewski, Guerilla Guide to CNC machining, mold making, and resin casting [website]

Papers

List of hallmark papers / new results I should read. This shouldn’t be interpreted as a list of must-read papers, since I’ve excluded many that I already know pretty well.

Manipulation

Representations
 
  • A. Zeng, P. Florence, J. Tompson, S. Welker, J. Chien, M. Attarian, T. Armstrong, I. Krasin, D. Duong, V. Sindhwani, J. Lee, Transporter Netowrks: Rearranging the Visual World for Robotic Manipulation [pdf]
  • M. Vecerik, J. Regli, O. Sushknov, D. Barker, R. Pevceviciute, T. Rhthorl, C. Schuster, R. Hadsell, L. Agapito, J. Scholtz, S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency [pdf]
Trends
 
  • A. Zeng, S. Song, J. Lee, A. Rodriguez, T. Funkhouser, TossingBot: Learning to Throw Arbitrary Objects with Residual Physics [pdf]
  • K. Zakka, A. Zeng, J. Lee, S. Song, Form2Fit: Learning Shape Priors for Generalized Assembly from Disassembly [pdf]
  • R. Jeong, J.T. Springenberg, J. Kay, D. Zheng, Y. Zhou, A. Galashov, N. Heess, F. Nori, Learning Dexterous Manipulation from Suboptimal Experts [pdf]

Intuitive Physics

Traditional Simulation Study
 
  • R. Featherstone, Robot Dynamics: Equations of Algorithms [pdf]
  • B.V. Mirtich, Impulse-based Dynamic Simulation of Rigid Body Systems [pdf]
Intuitive Physics
 
  • C.J. Bates, I. Yildrim, J.B. Tenenbaum, P. Battaglia, Modeling Human Intuitions about Liquid Flow with Particle-based Simulation [pdf]
  • Y. Li, J. Wu, R. Tedrake, J.B. Tenenbaum, A. Torralba, Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids [pdf]
  • J. Wu, I. Yildrim, J.J. Lim, W.T. Freeman, J.B. Tenenbaum, Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning [pdf]
  • E. Heiden, D. Millard, E. Coumans, Y. Sheng, G. Sukhatme, NeuralSim: Augmenting Differentiable Simulators with Neural Networks [pdf]

Policy Search

Model-based Policy Search (Synthesis)
 
  • K. Chatzilygeroudis, V. Vassilades, F. Stulp, S. Calinon, J.B. Mouret, A Survey on policy search algorithms for learning robot controllers in a handful of trials [pdf]
  • S. Levine, V. Koltun, Guided Policy Search [pdf]
  • R. Deits, T. Koolen, R. Tedrake, LVIS: Learning from Value Function Intervals for Contact-Aware Robot Controllers [pdf]
  • B. Recht, A Tour of Reinforcement Learning: The View from Continuous Control [pdf]
  • A. Rajeswaran, I. Mordatch, V. Kumar, A Game Theoretic Framework for Model-Based Reinforcement Learning [pdf] 
Policy Space Methods
 
  • J. Schulmann, S. Levine, P. Moritz, M. Jordan, P. Abbeel, Trust Region Policy Optimization [pdf]
  • J. Schulmann, F. Wolski, P. Dhariwal, A. Radford, O. Klimov, Proximal Policy Optimization Algorithms [pdf]
Risk-Sensitive Control
 
  • P. Whittle, Risk Sensitivity, A Strangely Pervasive Concept [pdf]
  • M. James, An Overview of Risk-Sensitive Stochastic Optimal Control [pdf]
Robust Policies / Simultaneous Stabilization
 
  • J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, P. Abbeel, Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World [pdf]
  • C. Finn, P. Abbeel, S. Levine, Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks [pdf]
  • I. Mordatch, K. Lowrey, E. Todorov, Ensemble-CIO: Full-Body Dynamic Motion Planning that Transfers to Physical Humanoids [pdf]
  • J. Won, J. Lee, Learning Body Shape Variation in Physics-based Characters [pdf]
  • X.B. Peng, M. Andrychowicz, W. Zaremba, P. Abbeel, Sim-to-Real Transfer of Robotic Control with Dynamics Randomization [pdf]
  • W. Yu, J. Tan, C. K. Liu, G. Turk, Preparing for the Unknown: Learning a Universal Policy with Online System Identification [pdf]
  • Workshop on Closing the Reality Gap in Sim2Real Transfer in Robotics [link]
Imitation Learning
 
  • S. Ross, G.J. Gordon, J.A. Bagnell, A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning (a.k.a. Dagger) [pdf]

State Abstraction / Representation Learning

 
  • D. Abel, A Theory of Abstraction in Reinforcement Learning [pdf]
  • A. Zhang, R. McAllister, R. Calandra, Y. Gal, S. Levine, Learning Invariant Representations for Reinforcement Learning without Reconstruction [pdf]
  • J. Subramanian, A. Sinha, R. seraj, A. Mahajan, Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems [pdf]

Deep Learning & Vision

Learning Theory & Trends
 
  • M. Belkin, D. Hsu, S. Ma, S. Mandal, Reconciling modern machine learning practice and the bias-variance trade-off [pdf]
  • J. Frankle, M. Carbin, The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks [pdf]
  • C. Zhang, S. Bengio, M. Hardt, B. Recht, O. Vinyals, Understanding Deep Learning requires rethinking generalization [pdf]
  • J. Lee, M. Simchowitz, M. Jordan, B. Recht, Gradient Descent Only Converges to Minimizers [pdf]
  • H. Mania, A. Guy, B. Recht, Simple Random Search of Static Linear Policies is Competitive for Reinforcement Learning [pdf]
Vision Papers & Trends
 
  • R. Zhang, P. Isola, A.A. Efros, E. Schechtman, O. Wang, The Unreasonable Effectiveness of Deep Features as a Perceptual Metric [pdf]
  • H. Fan, H. Su, L. Guibas, A Point Set Generation Network for 3D Object Reconstruction from a Single Image [pdf] 
  • Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, J. Xiao, 3D ShapeNets: A Deep Representation for Volumetric Shapes [pdf]
Sequence Learning (RNN, LSTM, Transformers)
3D Vision, Dense Reconstruction, SLAM

Simulators

List of robot simulators and comparisons.

Simulator Usage Collision Constraint Solver Contacts Integrator Coordinates Soft Body Support
Bullet Graphics MLCP Hard/Soft Semi-implicit Euler Minimal No
Drake Robotics Penalty Soft Error-Controlled Minimal No
DART Robotics LCP Hard Semi-implicit Euler Minimal No
Simbody Robotics Penalty Soft Minimal No
OpenDE Graphics LCP Hard Semi-implicit Euler Maximal No
Mujoco Robotics PGS on Soft LCP Soft RK4 Minimal No
FleX Graphics PGS Hard Position-based Dynamics HyperMaximal Yes

Hardware

Links to various vendors for robotics.

General Hardware

Old-reliable.

 
  • McMaster-Carr [website]
  • Stock Drive Products / Sterling Instruments (SDP/SI) [website]
  • 80/20 Inc. [website]

DIY / Hobby Components

For self-funded projects.

Electronics
 
Robotics & Motion
 
Chinese Mass Vendors
 

Professional Grade Components

For professional-graded funded robots. 

Motors & Gearboxes
 
Motion Controllers
 
Microcontrollers (MCU)
 
Microprocessors(MPU)
 
Depth Cameras
 
IMU / GPS Sensors

Manufacturing

Please don’t be like me and just go to the local machine shop.

Desktop CNC Mills
 
3D Printers
 
Desktop 2D Cutters
 
  • Disclaimer: I’ve never tried these and and remain skeptical on having them at home.
  • GlowForge laser cutter [website]
  • Wazer water jet [website]
PCB Vendors