Balaguer, Carlos

Bonsignorio, Fabio

Cangelosi, Angelo

Marocco, Davide

Stoelen, Martin F.

German

BieColl - Bielefeld eCollections

Hebb-like Learning for the Grounding of High-Level Symbols inSensorimotor TrajectoriesMartin F. Stoelen1, Davide Marocco2, Angelo Cangelosi2, Fabio Bonsignorio1,3, Carlos Balaguer11RoboticsLab, Universidad Carlos III de Madrid, Madrid, Spain2Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, UK3Heron Robots, Genova, Italy(mstoelen,fbonsign,balaguer)@ing.uc3m.es, (davide.marocco,acangelosi)@plymouth.ac.ukAbstractThe symbol grounding problem [1] is currently an active topicin both cognitive modeling and robotics. It refers to the need forgrounding the symbols used to represent thoughts and beliefs insomething other than just more symbols. This paper describeswork on an artificial neural network that grounds symbols insensorimotor trajectories through a local Hebb-like learningperformed online. This is of interest for exploring the develop-ment of higher-level cognitive abilities in humans through ex-periments with robots [2]. For example the grounding of actionwords in the sensorimotor interaction with the world [3]. It isalso relevant for assistive robots. Here it may be desirable tolearn online the correlation of multimodal inputs over time [4].An architecture with a time-delayed input structure and nohidden layers was used. Each input for each time delay was rep-resented by a set of neurons, the number of which depended onthe discretization desired. A Gaussian distribution was used todistribute activation over neurons for a given input value. Eachdelayed input layer was given full connectivity with a layer rep-resenting the current time inputs. A Hebb-like [5] learning rulewas then used to associate all inputs in the past with all in-puts in the present online. No activation was propagated dur-ing training of the neural network. Causality was thus assumedto arise implicitly from the time-delayed input structure of theneural network and its embedding in the sensorimotor loop. Thelearned weights were then used to predict into the future by onetime delay value, by propagating the activation resulting frominputs in the past and present. Predicting low-level actuation aswell as the high-level descriptive labels grounded in the low-level actuation. The ability to learn online distinguishes theapproach from recent work on Multiple Time-scales RecurrentNeural Networks (MTRNN) [6].First results from benchmarking trials on a simulated iCubhumanoid robot [7] are presented. A set of Cartesian trajectorieswere executed with one 7 Degree Of Freedom (DOF) arm, and 4descriptive labels where given to different phases of the trajecto-ries. Six DOF were actuated and used as low-level inputs. Twooverlapping number 8 shape movements were learned, rotated90 degrees with respect to one another. The labels indicatedthe top/bottom and left/right part of the trajectories, as well asthe respective movement directions. During prediction, number8 shapes that were 25% faster and 25% smaller were also at-tempted. For the worst case scenario used the root mean squareerror for the prediction of the joint angles was kept within 7degrees and the labels were predicted correctly more than 80%of the time. This indicates that the approach is reasonably ro-bust to the ambiguity introduced by partially overlapping andnon-exact trajectories.Index Terms: Symbol grounding, Hebb rule, sensorimotor co-ordination, developmental robotics, assistive robotics, bench-marking, online learning1. AcknowledgementsThis work was supported by the European Commission FP7Project ITALK (ICT-214668) within the Cognitive Systems andRobotics unit (FP7 ICT Challenge 2).2. References[1] Harnad, S., “The Symbol Grounding Problem”, Physica D: Non-linear Phenomena, 42:335-346, 1990.[2] Asada, M., Macdorman, K.F., Ishiguro, H., and Kuniyoshi, Y.,“Cognitive Developmental Robotics As a New Paradigm for theDesign of Humanoid Robots”, Robotics and Autonomous Sys-tems, 37:185-193, 2001.[3] Marocco, D., Cangelosi, A., Fischer, K., and Belpaeme, T.,“Grounding Action Words in the Sensorimotor Interaction withthe World: Experiments with a Simulated iCub HumanoidRobot”, Frontiers in neurorobotics, 4, 2010.[4] Balaguer, C., Jardón, A., Monje, C.A., Bonsignorio, F., Stoelen,M.F., Martı́nez, S. and Victores, J.G., “SULTAN: SimultaneousUser Learning and Task Execution, and its Application in Assis-tive Robotics”, in Workshop on New and Emerging Technologiesin Assistive Robotics at IROS 2011, San Francisco, CA, 2011.[5] Hebb, D.O., “The Organization of Behavior: A neuropsychologi-cal theory”. New York: Wiley, 1949.[6] Yamashita, Y. and Tani, J., “Emergence of functional hierarchyin a multiple timescale neural network model: a humanoid robotexperiment”, PLoS Computational Biology, vol. 4, no. 11, 2008.[7] Tikhanoff, V., Cangelosi, A., Fitzpatrick, P., Metta, G., Natale, L.,and Nori, F., “An Open-Source Simulator for Cognitive RoboticsResearch: the Prototype of the iCub Humanoid Robot Simulator,”in Proceedings of IEEE Workshop on Performance Metrics forIntelligent Systems Workshop (PerMIS’08), eds R. Madhavan andE. R. Messina (Washington, DC), 2008.Proceedings of the Post-Graduate Conference on Robotics and Development of Cognition 10-12 September 2012, Lausanne, Switzerland61

Hebb-like Learning for the Grounding of High-Level Symbols in Sensorimotor Trajectories

https://biecoll.ub.uni-bielefeld.de/index.php/robotdoc/article/download/357/450

Hebb-like Learning for the Grounding of High-Level Symbols in Sensorimotor Trajectories

Abstract

Similar works

Full text

Available Versions

BieColl - Bielefeld eCollections