How to Train Your Robot is a long term side project. I've been working on it in some form for 20 years. My lifetime goal is to make a robot as smart as my pup. It's a long road, but it has a lot of fascinating stops like machine learning, Python development, software engineering, and robotics. Come join me.
Chapter 1: Can't AI Already Do That?
Chapter 2: Keeping Time with Python
Chapter 3: Getting Processes to Talk to Each Other
Chapter 4: Making Animations with Matplotlib
Chapter 5: Simulating the Physical World
Chapter 6: Making Your Python Code Run Faster
Chapter 1: Can't AI Already Do That?
- 5. AI is still not as smart as my Shih Tzu.
- 8. Unsupervised learning
- 13. Supervised learning
- 18. Reinforcement learning
- 19. Rewards
- 20. Labels
- 24. Reward engineering
- 28. Human-Directed Reinforcement Learning (HDRL)
- 31. Sample efficiency
Chapter 2: Keeping Time with Python
- 5. The wall clock
- 8. Measuring time
- 17. Python
- 19. Platform (in)dependence
- 21. time.time()
- 25. Timing code
- 35. time.sleep()
- 42. Pacing code
- 48. The Pacemaker class
Chapter 3: Getting Processes to Talk to Each Other
- 6. Why muliple processes?
- 8. Why not threads?
- 10. Case study: Counting people
- 11.The getkey package
- 14. Python environment management
- 16. Getting keypresses
- 21. multiprocessing.Queue
- 27. Connecting processes with a Queue
- 33. Logging
- 34. Over-engineering vs under-engineering
- 42. Logging with JSON
- 53. Linting with flake8 and autoformatting with black
- 60. Reporting
- 69. Shell scripting
Chapter 4: Making Animations with Matplotlib
- 5. Case Study: Lissajous oscillations
- 8. Animation and robotics
- 9. Frame rate
- 12. Matplotlib
- 13. Object-Oriented Programming
- 20. Drawing with Matplotlib
- 22. Animation with Matplotlib
- 26. Colors
- 29. Order and depth
- 36. Patches
- 41. Translation
- 44. Scaling
- 45. Anchors
- 51. Rotation
- 61. Point to point movement
- 69. Minimum-jerk speed profiles
- 74. Logit-normal speed profiles
Chapter 5: Simulating the Physical World
- 6. Case study: Tetris shapes
- 7. Force = mass x acceleration
- 10. Numerical differentiation
- 14. Numerical integration
- 21. Separate processes for simulation and animation
- 26. Platform (in)dependence
- 32. Modeling atom-to-atom contact
- 39. Modeling atom-to-wall contact
- 45. Pitfalls of time discretization
- 48. Correcting for time discretization
- 53. Friction and inelasticity
- 60. Objects that are not circles
- 70. Stationary features
- 72. Initializating objects
Chapter 6: Making Your Python Code Run Faster
- 4. When to optimize
- 9. Profiling
- 10. py-spy
- 14. Vectorization
- 23. Numba
- 28. For-loops in Numba
- 29. @jit and @njit
- 31. Types
- 34. Preallocation
- 36. Incremental development
- 37. Matrix multiplication
- 39. The Ten Suggestions for working with Numba
- 43. Other optimzation methods
- 45. Monitoring
- 46. Choosing a metric
- 49. Aggregate metrics
- 52. A separate monitoring process
- 57. The Dashboard
- 63. Robust multi-process shutdown