Speeding Up Optimization-based Motion Planning through Deep Learning
Author:
Affiliation:
1. DLR Institute of Robotics & Mechatronics,Germany
2. Technical University of Munich,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9981026/9981028/09981717.pdf?arnumber=9981717
Reference23 articles.
1. Memory of Motion for Warm-Starting Trajectory Optimization
2. Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation
3. 3D modeling, distance and gradient computation for motion planning: A direct GPGPU approach
4. Harnessing Reinforcement Learning for Neural Motion Planning
5. The Repetition Roadmap for Repetitive Constrained Motion Planning
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