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Direct Policy Optimization using Deterministic Sampling and Collocation
- Publication Year :
- 2020
-
Abstract
- We present an approach for approximately solving discrete-time stochastic optimal-control problems by combining direct trajectory optimization, deterministic sampling, and policy optimization. Our feedback motion-planning algorithm uses a quasi-Newton method to simultaneously optimize a reference trajectory, a set of deterministically chosen sample trajectories, and a parameterized policy. We demonstrate that this approach exactly recovers LQR policies in the case of linear dynamics, quadratic objective, and Gaussian disturbances. We also demonstrate the algorithm on several nonlinear, underactuated robotic systems to highlight its performance and ability to handle control limits, safely avoid obstacles, and generate robust plans in the presence of unmodeled dynamics.<br />final revisions for RA-L
- Subjects :
- FOS: Computer and information sciences
0209 industrial biotechnology
Mathematical optimization
Control and Optimization
Computer science
Gaussian
Biomedical Engineering
02 engineering and technology
010501 environmental sciences
Collocation (remote sensing)
01 natural sciences
Computer Science::Robotics
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Computer Science - Robotics
020901 industrial engineering & automation
Artificial Intelligence
0105 earth and related environmental sciences
Underactuation
Stochastic process
Mechanical Engineering
Sampling (statistics)
Approximation algorithm
Trajectory optimization
Computer Science Applications
Human-Computer Interaction
Control and Systems Engineering
Trajectory
symbols
Computer Vision and Pattern Recognition
Robotics (cs.RO)
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....6d567de9d61008cb758d903e97fd6e8c