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Artificial neural networks for spatial perception: Towards visual object localisation in humanoid robots
2013
The 2013 International Joint Conference on Neural Networks (IJCNN)
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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. ...
CONCLUSIONS In this paper we investigate artificial neural networks (ANN) and their applicability to the spatial perception problem in robots. ...
doi:10.1109/ijcnn.2013.6706819
dblp:conf/ijcnn/LeitnerHFFS13
fatcat:ivsoifv6pbflrayhhsjgtuolqe
Transferring spatial perception between robots operating in a shared workspace
2012
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
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While the iCub moves it observes the object, and a neural network then learns how to relate its pose and visual inputs to the object location. ...
We use a Katana robotic arm to teach an iCub humanoid robot how to perceive the location of the objects it sees. ...
RESULTS The trained neural networks allow to estimate the position of the object in 3D space, with a high enough accuracy to allow for grasping experiments. ...
doi:10.1109/iros.2012.6385642
dblp:conf/iros/LeitnerHFFS12
fatcat:olkudkitifhwbhz3i42yv5argm
Reactive Reaching and Grasping on a Humanoid - Towards Closing the Action-Perception Loop on the iCub
2014
Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics
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An important feature is that the system can avoid obstacles -other objects detected in the visual stream -while reaching for the intended target object. ...
Its functionality is showcased by having our iCub humanoid robot pick-up objects from a table in front of it. ...
FP7-IST-IP-231722, 'Intrinsically Motivated Cumulative Learning Versatile Robots' (IM-CLeVeR). ...
doi:10.5220/0005113401020109
dblp:conf/icinco/LeitnerFFS14
fatcat:iao4niifsbdr7iw5lum6hns7vm
39th European Conference on Visual Perception (ECVP) 2016 Barcelona
2016
Perception
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Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. ...
and visual discomfort. [1] Li, Network-Comp Neural, 1999 ...
Humans, and possibly many other animals, use shading as a cue towards object-shape. ...
doi:10.1177/0301006616671273
fatcat:el2dmsuk6zd25pedijucuexl5m
Learning Spatial Object Localization from Vision on a Humanoid Robot
2012
International Journal of Advanced Robotic Systems
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We present a combined machine learning and computer vision approach for robots to localize objects. ...
It allows our iCub humanoid to quickly learn to provide accurate 3D position estimates (in the centimetre range) of objects seen. ...
The authors would like to thank: Leo Pape (IDSIA) & Ugo Pattacini (IIT) for the Cartesian controller and stereo camera calibration of the iCub; Davide Migliore & Alexandre Bernardino (IST) for helping ...
doi:10.5772/54657
fatcat:n2tbhq2ze5cvtl35o2vluoczc4
The State of Lifelong Learning in Service Robots: Current Bottlenecks in Object Perception and Manipulation
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2003.08151v2 fatcat:h5y4oajk7jbk7jhdtcbiqafayeOther Versions
Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. ...
In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. ...
In object perception, it applies to object representation, differentiating between object descriptors that are either hand-crafted or trained by a neural network. ...
arXiv:2003.08151v3
fatcat:ks4t3qfq3bhszgbb6lzrwwq3iq
How instructions modify perception: An fMRI study investigating brain areas involved in attributing human agency
2010
NeuroImage
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Behavioural results suggested that agency instructions influenced participants' perceptions of the stimuli. ...
The fMRI analysis indicated different functions within the paracingulate cortex: ventral paracingulate cortex was more active for human compared to computer agency instructed trials across all stimulus ...
Acknowledgments We would like to thank Leif Johansson for recording the actions for the point-light animations; Zoe Kourtzi for the helpful discussions in the design stages of this process, and, along ...
doi:10.1016/j.neuroimage.2010.04.025
pmid:20398769
pmcid:PMC2887490
fatcat:ij7gfd3khff2je7tzqdnmpynti
The State of Lifelong Learning in Service Robots:
2021
Journal of Intelligent and Robotic Systems
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AbstractService robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. ...
In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. ...
In object perception, it applies to object representation, differentiating between object descriptors that are either hand-crafted or trained by a neural network. ...
doi:10.1007/s10846-021-01458-3
fatcat:eeunivdvmrcg3piyx3r3pdsviq
Robot emotions generated and modulated by visual features of the environment
2013
2013 IEEE Symposium on Computational Intelligence for Creativity and Affective Computing (CICAC)
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Pilot experiments demonstrate how a humanoid robot tries to learn through interaction with a human companion to express emotions associated with different environmental scenes in a (near) human-like manner ...
A new and challenging task is to emulate emotional responses on a robot that are caused by visual stimuli, such that the robot's responses mirror that of the human user. ...
In this study, the artificial neural network consists only of one layer of weights, i.e. with no hidden layer of units. ...
doi:10.1109/cicac.2013.6595215
dblp:conf/cicac/WongNHCW13
fatcat:tchqlpf5pjedxdawll6dphjvje
Enhanced Robot Speech Recognition Using Biomimetic Binaural Sound Source Localization
2018
IEEE Transactions on Neural Networks and Learning Systems
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2019 pre-print arXiv:1902.05446v1 fatcat:dkfph6l3krfe5apng3rgayo5s4
More specifically, a robot orients itself toward the angle where the signal-to-noise ratio (SNR) of speech is maximized for one microphone before doing an ASR task. ...
Then, a feedforward neural network is used to handle high levels of ego noise and reverberation in the signal. Finally, the sound signal is fed into an ASR system. ...
An important advantage of our biomimetic neural representation of spatial cues is that it can be directly integrated with vision for audio-visual spatial attention [58] . ...
doi:10.1109/tnnls.2018.2830119
pmid:29993561
fatcat:wpyurosalfgs5aec5titoklkbq
Physiologie de la perception et de l'action
2010
Annuaire du Collège de France : Résumé Des Cours et Travaux
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critical period for orientation plasticity in the cat visual cortex », PLoS One , 2009, 4:e5380. ...
Ouvrages et chapitres d’ouvrages collectifs 94— Benchenane K., Zugaro M.B., Wiener S.I.: « Neural Bases of Spatial Learning and Memory », in Binder M.D, Hirokawa N., Windhorst U. ...
doi:10.4000/annuaire-cdf.358
fatcat:iqvywnguozhavcd3sfggppaxdu
Haptic SLAM for context-aware robotic hand prosthetics - simultaneous inference of hand pose and object shape using particle filters
2015
2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)
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We present a computational model for haptic exploration and shape reconstruction derived from mobile robotics: simultaneous localisation and mapping (SLAM). ...
In conjunction with tactile-enabled prostheses, this could allow for online object recognition and pose adaptation for more natural prosthetic control. ...
Here, we present a physics model of a humanoid hand, with 21 degrees of freedom and draw parallels between the problem of simultaneous localisation and mapping (SLAM) in robotics [3] , [4] and mapping ...
doi:10.1109/ner.2015.7146724
dblp:conf/ner/BehbahaniTTF15
fatcat:u5cffkjw3zcxbhbzmfdgdverhy
2020 Index IEEE Robotics and Automation Letters Vol. 5
2020
IEEE Robotics and Automation Letters
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., +, LRA April 2020 676-682 Visual Object Search by Learning Spatial Context. ...
., +, LRA April 2020 1835-1842 Deep Neural Network Approach in Robot Tool Dynamics Identification for Bilateral Teleoperation. ...
doi:10.1109/lra.2020.3032821
fatcat:qrnouccm7jb47ipq6w3erf3cja
Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes
2014
Connection science
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In this paper, we propose a model of learning multisensory integration based on an unsupervised learning algorithm in which an artificial neural network learns the noise characteristics of each of its ...
We report on a neurorobotic experiment in which we apply our algorithm to multi-sensory integration in a humanoid robot to demonstrate its effectiveness and compare it to human multi-sensory integration ...
To validate our model, we show in Section 3 that the algorithm can be used effectively in a real-world scenario by testing it in a robotic audio-visual object localisation task and demonstrate that audio-visual ...
doi:10.1080/09540091.2014.971224
fatcat:nr4w7fbyczhmxkt5rcpzhox7dm
The Robot Vibrissal System: Understanding Mammalian Sensorimotor Co-ordination Through Biomimetics
[chapter]
2015
Sensorimotor Integration in the Whisker System
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We also demonstrate how the appropriate co-ordination of these sub-systems, with a model of brain architecture, can give rise to integrated behaviour in a life-like whiskered robot. ...
We consider the problem of sensorimotor co-ordination in mammals through the lens of vibrissal touch, and via the methodology of embodied computational neuroscienceÑusing biomimetic robots to synthesize ...
Science project ÒDevelopment of motor-sensory strategies for vibrissal active touchÓ. ...
doi:10.1007/978-1-4939-2975-7_10
fatcat:v6332q6usjbfrip325kqmy7yfi
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