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