TY - GEN
T1 - Artificial neural networks for spatial perception: Towards visual object localisation in humanoid robots
AU - Leitner, Jurgen
AU - Harding, Simon
AU - Frank, Mikhail
AU - Forster, Alexander
AU - Schmidhuber, Jurgen
N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14
PY - 2013/12/1
Y1 - 2013/12/1
N2 - In this paper, we present our on-going research to allow humanoid robots to learn spatial perception. We are using artificial neural networks (ANN) to estimate the location of objects in the robot's environment. The method is using only the visual inputs and the joint encoder readings, no camera calibration and information is necessary, nor is a kinematic model. We find that these ANNs can be trained to allow spatial perception in Cartesian (3D) coordinates. These lightweight networks are providing estimates that are comparable to current state of the art approaches and can easily be used together with existing operational space controllers. © 2013 IEEE.
AB - In this paper, we present our on-going research to allow humanoid robots to learn spatial perception. We are using artificial neural networks (ANN) to estimate the location of objects in the robot's environment. The method is using only the visual inputs and the joint encoder readings, no camera calibration and information is necessary, nor is a kinematic model. We find that these ANNs can be trained to allow spatial perception in Cartesian (3D) coordinates. These lightweight networks are providing estimates that are comparable to current state of the art approaches and can easily be used together with existing operational space controllers. © 2013 IEEE.
UR - http://ieeexplore.ieee.org/document/6706819/
UR - http://www.scopus.com/inward/record.url?scp=84893584992&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2013.6706819
DO - 10.1109/IJCNN.2013.6706819
M3 - Conference contribution
SN - 9781467361293
BT - Proceedings of the International Joint Conference on Neural Networks
ER -