Cost-to-Go Function Generating Networks for High Dimensional Motion Planning
2021 IEEE International Conference on Robotics and Automation (ICRA)(2021)
Abstract
This paper presents c2g-HOF networks which learn to generate cost-to-go functions for manipulator motion planning. The c2g-HOF architecture consists of a cost-to-go function over the configuration space represented as a neural network (c2g-network) as well as a Higher Order Function (HOF) network which outputs the weights of the c2g-network for a given input workspace. Both networks are trained en...
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Key words
Costs,Three-dimensional displays,Neural networks,Computer architecture,Manipulators,Robot sensing systems,Planning
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