A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Approximate Inference-Based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models
2018
IEEE Robotics and Automation Letters
This work presents an efficient framework to generate a motion plan of a robot with high degrees of freedom (e.g., a humanoid robot). High-dimensionality of the robot configuration space often leads to difficulties in utilizing the widely-used motion planning algorithms, since the volume of the decision space increases exponentially with the number of dimensions. To handle complications arising from the large decision space, and to solve a corresponding motion planning problem efficiently, two
doi:10.1109/lra.2018.2856915
dblp:journals/ral/HaCC18
fatcat:oz77sp367jfedi5xwtbaab6dsi