Dynamic Robot Manipulation - Learned Optimization, Deformable Materials, and the Cloud

Dynamic Robot Manipulation - Learned Optimization, Deformable Materials, and the Cloud

Paul G. Allen School via YouTube Direct link

Robots of the Lost Arc Tasks

25 of 34

25 of 34

Robots of the Lost Arc Tasks

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Dynamic Robot Manipulation - Learned Optimization, Deformable Materials, and the Cloud

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  1. 1 Intro
  2. 2 Dynamic Robot Manipulation: Learned Optimization, Deformable Materials, and the Cloud Jeffrey Ichnowski, Ph.D.
  3. 3 Rigid World
  4. 4 Combine Grasp, Motion, and Dynamics
  5. 5 Planning for Dynamic Deformable
  6. 6 Use the Cloud
  7. 7 Trajectory and Optimization Discretization
  8. 8 Obstacle Constraint Linearization
  9. 9 Time Optimization
  10. 10 Science Robotics
  11. 11 Quadratic Programs in Robotics
  12. 12 OSQP Algorithm Overview
  13. 13 RLQP Reinforcement Learning QP Solver
  14. 14 Experiment Problem Classes Randomly generated QPs from OSQP benchmark suite
  15. 15 Benchmark Problems and Generalization
  16. 16 Analysis of a Learned Policy
  17. 17 Introducing Inertial Constraints
  18. 18 Suction Transport
  19. 19 Suction Constraint Analytic model
  20. 20 Suction Failures and Deformation
  21. 21 Suction Cup Deformation Constraint
  22. 22 Suction Gripper w/ Embedded Sensor
  23. 23 Learning a Suction Constraint
  24. 24 Suction Constraint Learning Pipeline
  25. 25 Robots of the Lost Arc Tasks
  26. 26 Problem Setup
  27. 27 Trajectory Parameterization
  28. 28 Sensitivity to Initial Conditions
  29. 29 Reset Motion
  30. 30 INDy: Self-Supervised Training
  31. 31 Cloud-based 72-core Motion Planning
  32. 32 Motion Planning Compute Requirements
  33. 33 Serverless Computing Limitations
  34. 34 Serverless Computing Environment

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