%0 Conference Paper %B 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems %D 2017 %T UJI RobInLab's Approach to the Amazon Robotics Challenge 2017 %A Angel P. del Pobil %A Majd Kassawat %A Angel J Duran %A Monica Arias %A Nataliya Nechyporenko %A Arijit Mallick %A Enric Cervera %A Dipendra Subedi %A Ilia Vasilev %A Daniel Cardin %A Emanuele Sansebastiano %A Ester Martinez-Martin %A Antonio Morales %A Gustavo A. Casañ %A Alejandro Arenal %A Bjorn Goriatcheff %A Carlos Rubert %A Gabriel Recatala %B 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems %I IEEE Xplore %C Daegu, Korea %G eng %0 Conference Paper %B International Conference on Intelligent Autonomous Systems %D 2016 %T Discovering the Relationship Between the Morphology and the Internal Model in a Robot System by Means of Neural Networks %A Angel J Duran %A del Pobil, Angel P %B International Conference on Intelligent Autonomous Systems %I Springer %G eng %0 Conference Paper %B Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on %D 2016 %T Initial weight estimation for learning the internal model based on the knowledge of the robot morphology %A Angel J Duran %A del Pobil, Angel P %B Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on %I IEEE %G eng %0 Conference Paper %B International Conference on Simulation of Adaptive Behavior %D 2016 %T A Model of Artificial Genotype and Norm of Reaction in a Robotic System %A Angel J Duran %A Angel P. del Pobil %B International Conference on Simulation of Adaptive Behavior %I Springer International Publishing %G eng %0 Conference Paper %B IEEE International Conference on Robotics and Automation (ICRA) %D 2015 %T Adaptive Saccade Controller Inspired by the Primates’ Cerebellum %A Antonelli, Marco %A Angel J Duran %A Eris Chinellato %A Angel P. del Pobil %K Biologically-Inspired Robots %K Control Architectures and Programming %K Learning and Adaptive Systems %X
Saccades are fast eye movements that allow humans and robots to bring the visual target in the center of the visual field. Saccades are open loop with respect to the vision system, thus their execution require a precise knowledge of the internal model of the oculomotor system. In this work, we modeled the saccade control, taking inspiration from the recurrent loops between the cerebellum and the brainstem. In this model, the brainstem acts as a fixed-inverse model of the oculomotor system, while the cerebellum acts as an adaptive element that learns the internal model of the oculomotor system. The adaptive filter is implemented using a state-of-the- art neural network, called I-SSGPR. The proposed approach, namely recurrent architecture, was validated through experiments performed both in simulation and on an antropomorphic robotic head. Moreover, we compared the recurrent architecture with another model of the cerebellum, the feedback error learning. Achieved results show that the recurrent architecture outperforms the feedback error learning in terms of accuracy and insensitivity to the choice of the feedback controller.
 
11:20-11:24, Paper FrA2T5.6 
%B IEEE International Conference on Robotics and Automation (ICRA) %C Seattle, Washington, USA %8 05/2015 %G eng %0 Conference Paper %B Humanoid Robots (Humanoids), 2015 IEEE-RAS 15th International Conference on %D 2015 %T Tombatossals: A humanoid torso for autonomous sensor-based tasks %A Felip, Javier %A Angel J Duran %A Antonelli, Marco %A Morales, Antonio %A Angel P. del Pobil %B Humanoid Robots (Humanoids), 2015 IEEE-RAS 15th International Conference on %I IEEE %G eng %0 Journal Article %J IEEE Trans. Auton. Mental Develop %D 2014 %T A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot %A Marco Antonelli %A Gibaldi, Agostino %A Beuth, Frederik %A Angel J Duran %A Canessa, Andrea %A Chessa, Manuela %A Solari, F %A Angel P. del Pobil %A Hamker, F %A Eris Chinellato %A Sabatini, SP %B IEEE Trans. Auton. Mental Develop %P 1–15 %G eng %R 10.1109/TAMD.2014.2332875 %0 Journal Article %J Robotics and Autonomous Systems %D 2014 %T Learning the visual-oculomotor transformation: Effects on saccade control and space representation %A Marco Antonelli %A Angel J Duran %A Eris Chinellato %A Angel P. del Pobil %K Cerebellum %K Gaussian process regression %K Humanoid robotics %K Sensorimotor transformation %K stereo vision %X

Active eye movements can be exploited to build a visuomotor representation of the surrounding environment. Maintaining and improving such representation requires to update the internal model involved in the generation of eye movements. From this perspective, action and perception are thus tightly coupled and interdependent. In this work, we encoded the internal model for oculomotor control with an adaptive filter inspired by the functionality of the cerebellum. Recurrent loops between a feed-back controller and the internal model allow our system to perform accurate binocular saccades and create an implicit representation of the nearby space. Simulations results show that this recurrent architecture outperforms classical feedback-error-learning in terms of both accuracy and sensitivity to system parameters. The proposed approach was validated implementing the framework on an anthropomorphic robotic head.

%B Robotics and Autonomous Systems %G eng %U http://www.sciencedirect.com/science/article/pii/S092188901400311X %R 10.1016/j.robot.2014.11.018 %0 Conference Proceedings %B International Joint Conference in Neural Networks %D 2013 %T Application of the Visuo-Oculomotor Transformation to Ballistic and Visually-Guided Eye Movements %A Marco Antonelli %A Angel J Duran %A Angel P. del Pobil %B International Joint Conference in Neural Networks %G eng %0 Book Section %B Designing Intelligent Robots: Reintegrating AI %D 2013 %T Integration of Visuomotor Learning, Cognitive Grasping and Sensor-Based Physical Interaction in the UJI Humanoid Torso %A Angel P. del Pobil %A Angel J Duran %A Marco Antonelli %A Javier Felip %A Antonio Morales %A M. Prats %A Eris Chinellato %B Designing Intelligent Robots: Reintegrating AI %I AAAI %V SS-13-04 %P pp. 6-11 %@ 978-1-57735-601-1 %G eng %0 Book Section %B Biomimetic and Biohybrid Systems %D 2013 %T Speeding-Up the Learning of Saccade Control %A Marco Antonelli %A Angel J Duran %A Eris Chinellato %A Angel P. del Pobil %E Lepora, NathanF. %E Mura, Anna %E Krapp, Holger G. %E Paul F. M. J. Verschure %E Tony J. Prescott %B Biomimetic and Biohybrid Systems %S Lecture Notes in Computer Science %I Springer Berlin Heidelberg %V 8064 %P 12-23 %@ 978-3-642-39801-8 %G eng %U http://dx.doi.org/10.1007/978-3-642-39802-5_2 %R 10.1007/978-3-642-39802-5_2