A Hybrid Planning Strategy through Learning from Vision for Target-directed Navigation

In this paper, we propose a goal-directed navigation system consisting of two planning strategies that both rely on vision but work on different scales. The first one works on a global scale and is responsible for generating spatial trajectories leading to the neighboring area of the target. It is a biologically inspired neural planning and navigation model involving learned representations of place and head-direction (HD) cells, where a planning network is trained to predict the neural activities of these cell representations given selected action signals. Recursive prediction and optimization of the continuous action signals generates goal-directed activation sequences, in which states and action spaces are represented by the population of place-, HD- and motor neuron activities. To compensate the remaining error from this look-ahead modelbased planning, a second planning strategy relies on visual recognition and performs target-driven reaching on a local scale so that the robot can reach the target with a finer accuracy. Experimental results show that through combining these two planning strategies the robot can precisely navigate to a distant target.

@InProceedings{ZWBW18,
 	 author =  {Zhou, Xiaomao and Weber, Cornelius and Bothe, Chandrakant and Wermter, Stefan},
 	 title = {A Hybrid Planning Strategy through Learning from Vision for Target-directed Navigation},
 	 booktitle = {International Conference on Artificial Neural Networks (ICANN)},
 	 journal = {None},
 	 editors = {}
 	 number = {}
 	 volume = {11140},
 	 pages = {304--311},
 	 year = {2018},
 	 month = {Oct},
 	 publisher = {Springer},
 	 doi = {10.1007/978-3-030-01421-6_30},
 }