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 [BibTeX] [Marc21]
Memory of Motion for Warm-starting Trajectory Optimization
Type of publication: Journal paper
Citation: Lembono_IEEERA-L_2020
Publication status: Published
Journal: IEEE Robotics and Automation Letters
Volume: 5
Number: 2
Year: 2020
Pages: 2594-2601
DOI: 10.1109/LRA.2020.2972893
Abstract: Trajectory optimization for motion planning requires good initial guesses to obtain good performance. In our proposed approach, we build a memory of motion based on a database of robot paths to provide good initial guesses. The memory of motion relies on function approximators and dimensionality reduction techniques to learn the mapping between the tasks and the robot paths. Three function approximators are compared: k-Nearest Neighbor, Gaussian Process Regression, and Bayesian Gaussian Mixture Regression. In addition, we show that the memory can be used as a metric to choose between several possible goals, and using an ensemble method to combine different function approximators results in a significantly improved warm-starting performance. We demonstrate the proposed approach with motion planning examples on the dual-arm robot PR2 and the humanoid robot Atlas.
Projects Idiap
Authors Lembono, Teguh Santoso
Paolillo, Antonio
Pignat, Emmanuel
Calinon, Sylvain
Editors Dongheui, Lee
Added by: [UNK]
Total mark: 0
  • Lembono_IEEERA-L_2020.pdf