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  1. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 19: Model-Based RLStanford OnlineNotes
  2. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 18: RL Policy OptimizationStanford OnlineNotes
  3. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 17: RL Value-Based MethodsStanford OnlineNotes
  4. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 16: Fundamentals of RLStanford OnlineNotes
  5. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 15: Imitation LearningStanford OnlineNotes
  6. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 14: Intro to IL and RLStanford OnlineNotes
  7. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 13: Intro to LearningStanford OnlineNotes
  8. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 12: Feasibility of MPCStanford OnlineNotes
  9. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 11: Introduction to MPCStanford OnlineNotes
  10. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 10: Reachibility AnalysisStanford OnlineNotes
  11. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 9: Stochastic Dyn. ProgramStanford OnlineNotes
  12. AStanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 8: LQR-Style AlgorithmsStanford OnlineNotes
  13. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 7: Dynamic ProgrammingStanford OnlineNotes
  14. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 6: Direct MethodsStanford OnlineNotes
  15. Stanford AA203 Optimal and Learning-Based Control | Spring 2026 | Lecture 5: Computational MethodsStanford OnlineNotes
  16. Louis Wiesmann: Efficient LiDAR-Based Mapping and Localization in Outdoor Environments (PhD Thesis)Cyrill StachnissNotes
  17. Lecture 15 - Optimization and Learning for Robot Control - Implementing MPC on a manipulatorOptimization and Learning for Robot Control 2025Notes
  18. Lecture 14 - Optimization and Learning for Robot Control - LAB Collision avoidanceOptimization and Learning for Robot Control 2025Notes
  19. Lecture 13 - Optimization and Learning for Robot Control - Model Predictive Control (part 2)Optimization and Learning for Robot Control 2025Notes
  20. Lecture 12 - Optimization and Learning for Robot Control - LAB Trajectory OptimizationOptimization and Learning for Robot Control 2025Notes
  21. Lecture 11 - Optimization and Learning for Robot Control - Model Predictive Control (part 1)Optimization and Learning for Robot Control 2025Notes
  22. Lecture 10 - Optimization and Learning for Robot Control - Trajectory Optimization: Direct MethodsOptimization and Learning for Robot Control 2025Notes
  23. Lecture 9 - Optimization and Leaning for Robot Control - Intro to Numerical OptimizationOptimization and Learning for Robot Control 2025Notes
  24. Talk by L. Chong: Zero-Shot Semantic Segmentation for Robots in Agriculture or "weed are wired"Cyrill StachnissNotes
  25. Lecture 8 - Optimization and Learning for Robot Control - Optimal control introductionOptimization and Learning for Robot Control 2025Notes
  26. Talk by N. Trekel: Benchmark for Evaluating Long-Term Localization in Indoor Environments (IROS'25)Cyrill StachnissNotes
  27. Lecture 7 - Optimization and Learning for Robot Control - Lab session on QP-based controlOptimization and Learning for Robot Control 2025Notes
  28. Talk by M. Sodano: 3D Hierarchical Panoptic Segmentation in Real Orchard Environments ... (IROS'25)Cyrill StachnissNotes
  29. DigiForest: Reconstructing trees in the forest with a hand-held sensor box.Cyrill StachnissNotes
  30. Lecture 6 - Learning and Optimization for Robot Control - QP-based motion control (part 2)Optimization and Learning for Robot Control 2025Notes
  31. LiDAR-Inertial Odometry Demo Using Multiple Multiple Platforms (RKO LIO by Meher Malladi et al.)Cyrill StachnissNotes
  32. Talk by Casado Herraez: SPR: Single-Scan Radar Place Recognition (RAL'24/ICRA'25)Cyrill StachnissNotes
  33. Talk by Y. Pan: PINGS Gaussian Splatting Meets Distance Fields (RSS2025)Cyrill StachnissNotes
  34. 1min PINGS Demo: Gaussian Splatting Meets Distance Fields within a Point-Based Implicit Neural MapCyrill StachnissNotes
  35. Trailer: Kinematic-ICP: Enhancing LiDAR Odometry with Kinematic Constraints for Wheeled ...(ICRA'25)Cyrill StachnissNotes
  36. Geodäsie in Bonn studieren? Kommt zum Tag der Geodäsie am Sa 24.05.2025! #Shorts #UniBonn #StudiumCyrill StachnissNotes
  37. Control-06: Model Predictive Control (M. Sondano)Cyrill StachnissNotes
  38. Control-05: Digital Control Systems (M. Sodano)Cyrill StachnissNotes
  39. Control-04: PID Control (M. Sodano)Cyrill StachnissNotes
  40. Control-03: Wheeled Mobile Robots: Kinematic Structures and Models + Control Problems (M. Sodano)Cyrill StachnissNotes
  41. Lecture 20: Path Planning of Robotic NeedleIntelligent Control of Robotic Systems By Prof. Felix OrlandoNotes
  42. Lecture 19: Sampling based Path Planning MethodsIntelligent Control of Robotic Systems By Prof. Felix OrlandoNotes
  43. Lecture 18: Introduction to Path PlanningIntelligent Control of Robotic Systems By Prof. Felix OrlandoNotes
  44. Lecture 17: Introduction to Search MethodsIntelligent Control of Robotic Systems By Prof. Felix OrlandoNotes
  45. Lecture 16: Introduction to Reinforcement LearningIntelligent Control of Robotic Systems By Prof. Felix OrlandoNotes
  46. Lecture 15: NN based Hybrid Force/Position Control of Robot ManipulatorIntelligent Control of Robotic Systems By Prof. Felix OrlandoNotes
  47. Lecture 14: Robust RBFNIntelligent Control of Robotic Systems By Prof. Felix OrlandoNotes
  48. Lecture 13: Neural Network based Feedback LinearizationIntelligent Control of Robotic Systems By Prof. Felix OrlandoNotes
  49. Lecture 12: Neural Adaptive Control of Robotics SystemsIntelligent Control of Robotic Systems By Prof. Felix OrlandoNotes
  50. Lecture 11: Neural Network based Robot ControlIntelligent Control of Robotic Systems By Prof. Felix OrlandoNotes