Computing machinery and intelligence, pp.433-460, 1950. ,
DOI : 10.1007/978-1-4020-6710-5_3
The Architecture of Human-Like General Intelligence, Theoretical Foundations of Artificial General Intelligence, pp.123-144, 2012. ,
DOI : 10.2991/978-94-91216-62-6_8
Fast and accurate recurrent neural network acoustic models for speech recognition, 2015. ,
Imagenet classification with deep convolutional neural networks " , in Advances in neural information processing systems, pp.1097-1105, 2012. ,
Concrete problems in ai safety, 2016. ,
A framework for attention and object categorization using a stereo head robot, XII Brazilian Symposium on Computer Graphics and Image Processing (Cat. No.PR00481), pp.143-152, 1999. ,
DOI : 10.1109/SIBGRA.1999.805719
Combination of foveal and peripheral vision for object recognition and pose estimation, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004, pp.5135-5140, 2004. ,
DOI : 10.1109/ROBOT.2004.1302532
Hierarchical temporal memory: Concepts, theory and terminology, 2006. ,
Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, vol.1, issue.4, pp.541-551, 1989. ,
DOI : 10.1007/BF00133697
Top-down influences on visual processing, Nature Reviews Neuroscience, vol.106, issue.5, pp.350-363, 2013. ,
DOI : 10.1152/jn.00429.2011
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3864796
ARTIFICIAL INTELLIGENCE: Autonomous Mental Development by Robots and Animals, Science, vol.291, issue.5504, pp.599-600, 2001. ,
DOI : 10.1126/science.291.5504.599
An open-source simulator for cognitive robotics research, Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems, PerMIS '08, pp.57-61, 2008. ,
DOI : 10.1145/1774674.1774684
Intrinsic Motivation Systems for Autonomous Mental Development, IEEE Transactions on Evolutionary Computation, vol.11, issue.2, pp.265-286, 2007. ,
DOI : 10.1109/TEVC.2006.890271
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.107.666
Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints, Intrinsically motivated learning in natural and artificial systems, pp.303-365, 2013. ,
DOI : 10.1007/978-3-642-32375-1_13
URL : https://hal.archives-ouvertes.fr/hal-00788611
Visual salience, Scholarpedia, vol.2, issue.9, p.3327, 2007. ,
DOI : 10.4249/scholarpedia.3327
URL : http://doi.org/10.4249/scholarpedia.3327
Graph-based visual saliency, Advances in neural information processing systems, pp.545-552, 2006. ,
Neural Network Fusion of Color, Depth and Location for Object Instance Recognition on a Mobile Robot, Computer Vision-ECCV 2014 Workshops, pp.791-805, 2014. ,
DOI : 10.1007/978-3-319-16199-0_55
URL : https://hal.archives-ouvertes.fr/hal-01087392
Apprentissage incrémental de la saillance visuelle pour des applications robotique, Journées francophones des jeunes chercheurs en vision par ordinateur, 2015. ,
Environment exploration for object-based visual saliency learning, 2016 IEEE International Conference on Robotics and Automation (ICRA), pp.3140-3148, 2016. ,
DOI : 10.1109/ICRA.2016.7487379
URL : https://hal.archives-ouvertes.fr/hal-01289159
On the use of intrinsic motivation for visual saliency learning, 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2016. ,
DOI : 10.1109/DEVLRN.2016.7846811
URL : https://hal.archives-ouvertes.fr/hal-01370850
ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, vol.1010, issue.1, 2014. ,
DOI : 10.1007/978-3-642-15555-0_11
URL : http://arxiv.org/abs/1409.0575
A flexible and scalable SLAM system with full 3D motion estimation, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, 2011. ,
DOI : 10.1109/SSRR.2011.6106777
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.302.2579
Learning opencv: Computer vision with the opencv library, 2008. ,
3D is here: Point Cloud Library (PCL), 2011 IEEE International Conference on Robotics and Automation, pp.1-4, 2011. ,
DOI : 10.1109/ICRA.2011.5980567
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.463.6458
Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, pp.675-678, 2014. ,
DOI : 10.1145/2647868.2654889
Ros: An open-source robot operating system, ICRA workshop on open source software, p.5, 2009. ,
A large-scale hierarchical multi-view RGB-D object dataset, 2011 IEEE International Conference on Robotics and Automation, pp.1817-1824, 2011. ,
DOI : 10.1109/ICRA.2011.5980382
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.190.1598
An In Depth View of Saliency, Procedings of the British Machine Vision Conference 2013, pp.2013-2022, 2013. ,
DOI : 10.5244/C.27.112
Computational modelling of visual attention, Nature Reviews Neuroscience, vol.2, issue.3, pp.194-203, 2001. ,
DOI : 10.1038/35058500
Human gaze control during real-world scene perception, Trends in cognitive sciences, pp.498-504, 2003. ,
DOI : 10.1016/j.tics.2003.09.006
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.545.5406
Computational visual attention systems and their cognitive foundations, ACM Transactions on Applied Perception, vol.7, issue.1, p.6, 2010. ,
DOI : 10.1145/1658349.1658355
State-of-the-art in visual attention modeling " , Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.35, issue.1, pp.185-207, 2013. ,
Eye Movements During Perception of Complex Objects, 1967. ,
DOI : 10.1007/978-1-4899-5379-7_8
Sights and sighs, 28688. ,
A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.11, pp.1254-1259, 1998. ,
DOI : 10.1109/34.730558
A coherent computational approach to model bottom-up visual attention, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.5, pp.802-817, 2006. ,
DOI : 10.1109/TPAMI.2006.86
URL : https://hal.archives-ouvertes.fr/hal-00669578
Saliency Detection: A Spectral Residual Approach, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007. ,
DOI : 10.1109/CVPR.2007.383267
Image signature: Highlighting sparse salient regions " , Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.34, issue.1, pp.194-201, 2012. ,
Attention based on information maximization, Journal of Vision, vol.7, issue.9, pp.950-950, 2007. ,
DOI : 10.1167/7.9.950
URL : http://doi.org/10.1167/7.9.950
DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations, IEEE Transactions on Image Processing, vol.26, issue.9, 2015. ,
DOI : 10.1109/TIP.2017.2710620
Boosting bottom-up and top-down visual features for saliency estimation, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.438-445, 2012. ,
DOI : 10.1109/CVPR.2012.6247706
An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World, The International Journal of Robotics Research, vol.73, issue.2, pp.133-154, 2010. ,
DOI : 10.1006/cviu.1998.0736
Vocus: A visual attention system for object detection and goal-directed search, 2006. ,
DOI : 10.1007/11682110
A proto-object based visual attention model " , in Attention in cognitive systems. Theories and systems from an interdisciplinary viewpoint, pp.198-215, 2007. ,
Traditional saliency reloaded: A good old model in new shape, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.82-90, 2015. ,
DOI : 10.1109/CVPR.2015.7298603
Q-learning of sequential attention for visual object recognition from informative local descriptors, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.649-656, 2005. ,
DOI : 10.1145/1102351.1102433
Predicting visual fixations on video based on low-level visual features, Vision Research, vol.47, issue.19, pp.2483-2498, 2007. ,
DOI : 10.1016/j.visres.2007.06.015
URL : https://hal.archives-ouvertes.fr/hal-00287424
RGBD Salient Object Detection: A Benchmark and Algorithms, Computer Vision?ECCV 2014, pp.92-109, 2014. ,
DOI : 10.1007/978-3-319-10578-9_7
Saliency-based object discovery on RGB-D data with a late-fusion approach, 2015 IEEE International Conference on Robotics and Automation (ICRA), pp.1866-1873, 2015. ,
DOI : 10.1109/ICRA.2015.7139441
Event-driven visual attention for the humanoid robot iCub, Neuromorphic Engineering Systems and Applications, p.23, 2015. ,
DOI : 10.3389/fnins.2013.00234
URL : http://doi.org/10.3389/fnins.2013.00234
Optimal eye movement strategies in visual search, Nature, vol.40, issue.7031, pp.387-391, 2005. ,
DOI : 10.1016/S0042-6989(02)00040-8
Simulating human saccadic scanpaths on natural images, CVPR 2011, pp.441-448, 2011. ,
DOI : 10.1109/CVPR.2011.5995423
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.663.7211
Stochastic bottom???up fixation prediction and saccade generation, Image and Vision Computing, vol.31, issue.9, pp.686-693, 2013. ,
DOI : 10.1016/j.imavis.2013.06.006
Saccadic model of eye movements for free-viewing condition, Vision Research, vol.116, pp.152-164, 2015. ,
DOI : 10.1016/j.visres.2014.12.026
URL : https://hal.archives-ouvertes.fr/hal-01204682
Introducing context-dependent and spatially-variant viewing biases in saccadic models, Vision Research, vol.121, pp.72-84, 2016. ,
DOI : 10.1016/j.visres.2016.01.005
URL : https://hal.archives-ouvertes.fr/hal-01391745
Learning a saliency map using fixated locations in natural scenes, Journal of Vision, vol.11, issue.3, p.9, 2011. ,
DOI : 10.1167/11.3.9
Overt visual attention for a humanoid robot, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180), pp.2332-2337, 2001. ,
DOI : 10.1109/IROS.2001.976418
Unifying visual saliency with hog feature learning for traffic sign detection, Intelligent Vehicles Symposium, pp.24-29, 2009. ,
A Saliency Detection Model Using Low-Level Features Based on Wavelet Transform, IEEE Transactions on Multimedia, vol.15, issue.1, pp.96-105, 2013. ,
DOI : 10.1109/TMM.2012.2225034
Attention Model Based SIFT Keypoints Filtration for Image Retrieval, Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008), pp.191-196, 2008. ,
DOI : 10.1109/ICIS.2008.24
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.581.8012
Modeling global scene factors in attention, Journal of the Optical Society of America A, vol.20, issue.7, pp.1407-1418, 2003. ,
DOI : 10.1364/JOSAA.20.001407
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.6.4093
Global contrast based salient region detection, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp.409-416, 2011. ,
DOI : 10.1109/cvpr.2011.5995344
URL : http://cg.cs.tsinghua.edu.cn/papers/PAMI-2014-Saliency.pdf
Saliency Detection: A Boolean Map Approach, 2013 IEEE International Conference on Computer Vision, pp.153-160, 2013. ,
DOI : 10.1109/ICCV.2013.26
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.650.7342
Visual saliency estimation by nonlinearly integrating features using region covariances, Journal of Vision, vol.13, issue.4, p.11, 2013. ,
DOI : 10.1167/13.4.11
URL : http://doi.org/10.1167/13.4.11
Learning Deep Features for Discriminative Localization, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.2016 ,
DOI : 10.1109/CVPR.2016.319
URL : http://arxiv.org/abs/1512.04150
Visual saliency based on multiscale deep features, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5455-5463, 2015. ,
Salient Pattern Detection Using W 2 on Multivariate Normal Distributions, 2012. ,
DOI : 10.1007/978-3-642-32717-9_25
Visual Saliency with Statistical Priors, International Journal of Computer Vision, vol.12, issue.6, pp.239-253, 2014. ,
DOI : 10.1167/12.6.1
Learning to predict where humans look, 2009 IEEE 12th International Conference on Computer Vision, pp.2106-2113, 2009. ,
DOI : 10.1109/ICCV.2009.5459462
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.151.6519
Visual saliency estimation by integrating features using multiple kernel learning, CoRR, vol.5693, 1307. ,
The emergence of attention by population-based inference and its role in distributed processing and cognitive control of vision, Computer Vision and Image Understanding, vol.100, issue.1-2, pp.64-106, 2005. ,
DOI : 10.1016/j.cviu.2004.09.005
Biologically motivated visual attention system using bottom-up saliency map and top-down inhibition, Neural Information Processing-Letters and Review, vol.2, issue.1, 2004. ,
Modeling visual attention via selective tuning, Artificial Intelligence, vol.78, issue.1-2, pp.507-545, 1995. ,
DOI : 10.1016/0004-3702(95)00025-9
URL : http://doi.org/10.1016/0004-3702(95)00025-9
Predicting human gaze using low-level saliency combined with face detection, Advances in neural information processing systems, pp.241-248, 2008. ,
Saliency model-based face segmentation and tracking in head-and-shoulder video sequences, Journal of Visual Communication and Image Representation, vol.19, issue.5, pp.320-333, 2008. ,
DOI : 10.1016/j.jvcir.2008.04.001
Context-Aware Saliency Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.10, pp.1915-1926, 2012. ,
DOI : 10.1109/TPAMI.2011.272
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.193.5770
Saliency detection by multi-context deep learning, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1265-1274, 2015. ,
DOI : 10.1109/CVPR.2015.7298731
SEarch via Recursive Rejection (SERR): A Connectionist Model of Visual Search, Cognitive Psychology, vol.25, issue.1, pp.43-110, 1993. ,
DOI : 10.1006/cogp.1993.1002
Deep gaze i: Boosting saliency prediction with feature maps trained on imagenet, 2014. ,
Is object localization for free? - Weakly-supervised learning with convolutional neural networks, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.685-694, 2015. ,
DOI : 10.1109/CVPR.2015.7298668
URL : https://hal.archives-ouvertes.fr/hal-01015140
Semiautomatic visual-attention modeling and its application to video compression, 2014 IEEE International Conference on Image Processing (ICIP), pp.1105-1109, 2014. ,
DOI : 10.1109/ICIP.2014.7025220
Cat2000: A large scale fixation dataset for boosting saliency research, 2015. ,
Learning to Detect A Salient Object, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.353-367, 2011. ,
DOI : 10.1109/CVPR.2007.383047
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.8621
A benchmark of computational models of saliency to predict human fixations, p.2012 ,
What do saliency models predict?, Journal of Vision, vol.14, issue.3, pp.14-14, 2014. ,
DOI : 10.1167/14.3.14
URL : http://doi.org/10.1167/14.3.14
Global Contrast Based Salient Region Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, issue.3, pp.569-582, 2015. ,
DOI : 10.1109/TPAMI.2014.2345401
URL : http://cg.cs.tsinghua.edu.cn/papers/PAMI-2014-Saliency.pdf
The Secrets of Salient Object Segmentation, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.280-287, 2014. ,
DOI : 10.1109/CVPR.2014.43
Quantifying center bias of observers in free viewing of dynamic natural scenes, Journal of Vision, vol.9, issue.7, pp.4-4, 2009. ,
DOI : 10.1167/9.7.4
Saliency and Human Fixations: State-of-the-Art and Study of Comparison Metrics, 2013 IEEE International Conference on Computer Vision, pp.1153-1160, 2013. ,
DOI : 10.1109/ICCV.2013.147
Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study, IEEE Transactions on Image Processing, vol.22, issue.1, pp.55-69, 2013. ,
DOI : 10.1109/TIP.2012.2210727
Active vision, International Journal of Computer Vision, vol.203, issue.4, pp.333-356, 1988. ,
DOI : 10.1109/TPAMI.1984.4767471
Focus of attention and gaze control for robot vision, 1995. ,
Inhibition of return, Trends in Cognitive Sciences, vol.4, issue.4, pp.138-147, 2000. ,
DOI : 10.1016/S1364-6613(00)01452-2
URL : https://hal.archives-ouvertes.fr/inserm-00000089
Visual Attention for Robotic Cognition: A Survey, IEEE Transactions on Autonomous Mental Development, vol.3, issue.1, pp.92-105, 2011. ,
DOI : 10.1109/TAMD.2010.2096505
Cue normalization schemes in saliency-based visual attention models, International cognitive vision workshop, 2006. ,
Loop closure detection in SLAM by combining visual and spatial appearance, Robotics and Autonomous Systems, vol.54, issue.9, pp.740-749, 2006. ,
DOI : 10.1016/j.robot.2006.04.016
Real-Time Visual SLAM for Autonomous Underwater Hull Inspection Using Visual Saliency, IEEE Transactions on Robotics, vol.29, issue.3, pp.719-733, 2013. ,
DOI : 10.1109/TRO.2012.2235699
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.649.5339
Curious George: An attentive semantic robot, Robotics and Autonomous Systems, vol.56, issue.6, pp.503-511, 2008. ,
DOI : 10.1016/j.robot.2008.03.008
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.309.4177
An Overt Visual Attention Mechanism Based on Saliency Dynamics, International Journal of Intelligent Computing in Medical Sciences & Image Processing, vol.2, issue.2, pp.93-100, 2008. ,
DOI : 10.1080/1931308X.2008.10644157
Peripheral-foveal vision for real-time object recognition and tracking in video, IJCAI, pp.2115-2121, 2007. ,
Robust object recognition with cortex-like mechanisms " , Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.29, issue.3, pp.411-426, 2007. ,
DOI : 10.1109/tpami.2007.56
URL : http://cbcl.mit.edu/projects/cbcl/publications/ps/serre-wolf-poggio-PAMI-07.pdf
You Only Look Once: Unified, Real-Time Object Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. ,
DOI : 10.1109/CVPR.2016.91
URL : http://arxiv.org/abs/1506.02640
Attend, infer, repeat: Fast scene understanding with generative models, 2016. ,
Vision in the real world: Finding, attending and recognizing objects, International Journal of Imaging Systems and Technology, vol.22, issue.5, pp.189-208, 2006. ,
DOI : 10.1002/0471725250
Learning sequential visual attention control through dynamic state space discretization, 2009 IEEE International Conference on Robotics and Automation, pp.2258-2263, 2009. ,
DOI : 10.1109/ROBOT.2009.5152543
A reinforcement learning model of selective visual attention, Proceedings of the fifth international conference on Autonomous agents , AGENTS '01, pp.457-464, 2001. ,
DOI : 10.1145/375735.376414
Object search and localization for an indoor mobile robot, CIT. Journal of Computing and Information Technology, vol.17, issue.1, pp.67-80, 2009. ,
Realtime face detection and tracking using a single Pan, Tilt, Zoom camera, 2008 23rd International Conference Image and Vision Computing New Zealand, pp.1-6, 2008. ,
DOI : 10.1109/IVCNZ.2008.4762103
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.1717
Most salient region tracking, 2009 IEEE International Conference on Robotics and Automation, pp.1869-1874, 2009. ,
DOI : 10.1109/ROBOT.2009.5152298
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.420.2812
Strategies for Object Manipulation using Foveal and Peripheral Vision, Fourth IEEE International Conference on Computer Vision Systems (ICVS'06), pp.50-50, 2006. ,
DOI : 10.1109/ICVS.2006.57
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.107.6220
Early integration of vision and manipulation, Proceedings of the International Joint Conference on Neural Networks, 2003., p.2703, 2003. ,
DOI : 10.1109/IJCNN.2003.1223994
Mobile robot localisation using active vision, European Conference on Computer Vision, pp.809-825, 1998. ,
DOI : 10.1007/BFb0054781
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.3996
Stochastic control for maximizing mutual information in active sensing, IEEE Int. Conf. on Robotics and Automation (ICRA) Workshop on Robots in Homes and Industry, 2014. ,
Learning to recognize objects through curiosity-driven manipulation with the iCub humanoid robot, 2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL), pp.1-8, 2013. ,
DOI : 10.1109/DevLrn.2013.6652525
URL : https://hal.archives-ouvertes.fr/hal-00919674
Integrating context-free and contextdependent attentional mechanisms for gestural object reference, International Conference on Computer Vision Systems, pp.22-33, 2003. ,
DOI : 10.1007/s00138-004-0157-2
A fast and scalable system for visual attention, object based attention and object recognition for humanoid robots, 2014 IEEE-RAS International Conference on Humanoid Robots, pp.316-321, 2014. ,
DOI : 10.1109/HUMANOIDS.2014.7041378
Vision for robotic object manipulation in domestic settings, Robotics and Autonomous Systems, vol.52, issue.1, pp.85-100, 2005. ,
DOI : 10.1016/j.robot.2005.03.011
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.216.2914
Towards multiview object class detection, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), pp.1589-1596, 2006. ,
DOI : 10.1109/cvpr.2006.311
URL : https://hal.archives-ouvertes.fr/inria-00548577
Autonomous acquisition of visual multi-view object representations for object recognition on a humanoid robot, 2010 IEEE International Conference on Robotics and Automation, pp.2012-2019, 2010. ,
DOI : 10.1109/ROBOT.2010.5509328
Speeding-up robot exploration by exploiting background information, IEEE Robotics and Automation Letters, vol.1, issue.2, pp.716-723, 2016. ,
Exploring objects for recognition in the real word, 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp.429-434, 2007. ,
DOI : 10.1109/ROBIO.2007.4522200
Active object recognition on a humanoid robot, 2012 IEEE International Conference on Robotics and Automation, pp.2012-2021 ,
DOI : 10.1109/ICRA.2012.6225218
From sensorimotor development to object perception, 5th IEEE-RAS International Conference on Humanoid Robots, 2005., pp.226-231, 2005. ,
DOI : 10.1109/ICHR.2005.1573572
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.363.8560
Developmental approach for interactive object discovery, The 2012 International Joint Conference on Neural Networks (IJCNN), pp.1-7, 2012. ,
DOI : 10.1109/IJCNN.2012.6252606
URL : https://hal.archives-ouvertes.fr/hal-00755298
Perception and human interaction for developmental learning of objects and affordances, 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), pp.1-8, 2012. ,
DOI : 10.1109/HUMANOIDS.2012.6651528
URL : https://hal.archives-ouvertes.fr/hal-00755297
Utilizing object-object and object-scene context when planning to find things, 2009 IEEE International Conference on Robotics and Automation, pp.2168-2173, 2009. ,
DOI : 10.1109/ROBOT.2009.5152831
Contextual object category recognition for RGB-D scene labeling, Robotics and Autonomous Systems, vol.62, issue.2, pp.241-256, 2014. ,
DOI : 10.1016/j.robot.2013.10.001
URL : http://elib.dlr.de/97147/1/FINAL_Published_Version_RAS14.pdf
Modeling attention to salient proto-objects, Neural Networks, vol.19, issue.9, pp.1395-1407, 2006. ,
DOI : 10.1016/j.neunet.2006.10.001
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.86.1493
Where to Look Next? Combining Static and Dynamic Proto-objects in a TVA-based Model of Visual Attention, Cognitive Computation, vol.5, issue.6, pp.326-343, 2010. ,
DOI : 10.1093/acprof:oso/9780198570943.001.0001
Is bottom-up attention useful for object recognition?, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., p.37, 2004. ,
DOI : 10.1109/CVPR.2004.1315142
URL : http://authors.library.caltech.edu/25202/1/RUTcvpr04.pdf
Unsupervised Object Class Discovery via Saliency-Guided Multiple Class Learning, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp.2012-3218 ,
DOI : 10.1109/TPAMI.2014.2353617
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.359.5533
A Bimodal Laser-Based Attention System, Computer Vision and Image Understanding, vol.100, issue.1-2, pp.124-151, 2005. ,
DOI : 10.1016/j.cviu.2004.08.005
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.4779
Autonomous switching of top-down and bottom-up attention selection for vision guided mobile robots, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.4009-4014, 2009. ,
DOI : 10.1109/IROS.2009.5354674
Deep learning for visual understanding: A review, Neurocomputing, vol.187, pp.27-48, 2016. ,
DOI : 10.1016/j.neucom.2015.09.116
Slic superpixels compared to state-of-the-art superpixel methods " , Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.34, issue.11, pp.2274-2282, 2012. ,
Feature selection and learning for semantic segmentation ,
Seeds: Superpixels extracted via energy-driven sampling, Computer Vision?ECCV 2012, pp.2012-2025 ,
Entropy rate superpixel segmentation, CVPR 2011, pp.2097-2104, 2011. ,
DOI : 10.1109/CVPR.2011.5995323
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.362.8407
Superpixels via pseudo-Boolean optimization, 2011 International Conference on Computer Vision, pp.1387-1394, 2011. ,
DOI : 10.1109/ICCV.2011.6126393
Voxel cloud connectivity segmentationsupervoxels for point clouds, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2027-2034, 2013. ,
DOI : 10.1109/cvpr.2013.264
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.673.482
Flattening Supervoxel Hierarchies by the Uniform Entropy Slice, 2013 IEEE International Conference on Computer Vision, pp.2240-2247, 2013. ,
DOI : 10.1109/ICCV.2013.279
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.406.1925
Depth-adaptive superpixels, Pattern Recognition (ICPR), 2012 21st International Conference on, pp.2087-2090, 2012. ,
Non-parametric Model for Background Subtraction, European conference on computer vision, pp.751-767, 2000. ,
DOI : 10.1007/3-540-45053-X_48
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.592.3233
Motion-based segmentation and region tracking in image sequences, Pattern Recognition, vol.34, issue.3, pp.661-670, 2001. ,
DOI : 10.1016/S0031-3203(00)00014-5
Efficient, dense, object-based segmentation from RGBD video, 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016. ,
DOI : 10.1109/ICRA.2016.7487380
Efficient Graph-Based Image Segmentation, International Journal of Computer Vision, vol.59, issue.2, pp.167-181, 2004. ,
DOI : 10.1023/B:VISI.0000022288.19776.77
Grabcut: Interactive foreground extraction using iterated graph cuts, ACM transactions on graphics (TOG), ACM, pp.309-314, 2004. ,
A comparison of geometric and energy-based point cloud semantic segmentation methods, 2013 European Conference on Mobile Robots, pp.88-93, 2013. ,
DOI : 10.1109/ECMR.2013.6698825
URL : https://hal.archives-ouvertes.fr/hal-00963863
Active visual segmentation " , Pattern Analysis and Machine Intelligence, IEEE Transactions On, vol.34, issue.4, pp.639-653, 2012. ,
Active segmentation with fixation, 2009 IEEE 12th International Conference on Computer Vision, pp.468-475, 2009. ,
DOI : 10.1109/ICCV.2009.5459254
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.431.1980
Object partitioning using local convexity, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.304-311, 2014. ,
DOI : 10.1109/cvpr.2014.46
Object discovery in 3D scenes via shape analysis, 2013 IEEE International Conference on Robotics and Automation, pp.2088-2095, 2013. ,
DOI : 10.1109/ICRA.2013.6630857
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.309.5887
Attention-driven object detection and segmentation of cluttered table scenes using 2.5 d symmetry, 2014 IEEE International Conference on Robotics and Automation (ICRA), pp.4946-4952, 2014. ,
Cluttered scene segmentation using the symmetry constraint, 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016. ,
DOI : 10.1109/icra.2016.7487376
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, vol.24, issue.6, pp.381-395, 1981. ,
DOI : 10.1145/358669.358692
The Pascal Visual Object Classes Challenge: A Retrospective, International Journal of Computer Vision, vol.34, issue.11, pp.98-136, 2015. ,
DOI : 10.1109/TPAMI.2012.204
Indoor Segmentation and Support Inference from RGBD Images, European Conference on Computer Vision, pp.746-760, 2012. ,
DOI : 10.1007/978-3-642-33715-4_54
What, where and how many? combining object detectors and crfs, European conference on computer vision, pp.424-437, 2010. ,
Semantic Segmentation of Urban Scenes Using Dense Depth Maps, European Conference on Computer Vision, pp.708-721, 2010. ,
DOI : 10.1007/978-3-642-15561-1_51
Superparsing, European conference on computer vision, pp.352-365, 2010. ,
DOI : 10.1109/TPAMI.2008.128
Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.564-571, 2013. ,
DOI : 10.1109/CVPR.2013.79
Deep convolutional networks for scene parsing, ICML 2009 Deep Learning Workshop, Citeseer, 2009. ,
Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling, 2015. ,
DOI : 10.1109/tpami.2016.2644615
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. ,
DOI : 10.1109/CVPR.2016.348
Weakly-and semi-supervised learning of a dcnn for semantic image segmentation, 2015. ,
Edge Boxes: Locating Object Proposals from Edges, Computer Vision?ECCV 2014, pp.391-405, 2014. ,
DOI : 10.1007/978-3-319-10602-1_26
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.453.5208
Robust real-time object detection, International Journal of Computer Vision, vol.4, 2001. ,
A discriminatively trained, multiscale, deformable part model, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008. ,
DOI : 10.1109/CVPR.2008.4587597
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.156.686
What Makes for Effective Detection Proposals?, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.4, pp.814-830, 2016. ,
DOI : 10.1109/TPAMI.2015.2465908
URL : http://arxiv.org/abs/1502.05082
Segmentation as selective search for object recognition, 2011 International Conference on Computer Vision, pp.1879-1886, 2011. ,
DOI : 10.1109/ICCV.2011.6126456
Measuring the objectness of image windows " , Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.34, issue.11, pp.2189-2202, 2012. ,
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Advances in Neural Information Processing Systems, pp.91-99, 2015. ,
DOI : 10.1109/TPAMI.2016.2577031
URL : http://arxiv.org/abs/1506.01497
Microsoft COCO: Common Objects in Context, European Conference on Computer Vision, pp.740-755, 2014. ,
DOI : 10.1007/978-3-319-10602-1_48
URL : http://arxiv.org/abs/1405.0312
LocNet: Improving Localization Accuracy for Object Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. ,
DOI : 10.1109/CVPR.2016.92
URL : https://hal.archives-ouvertes.fr/hal-01245707
Lsda: Large scale detection through adaptation, Advances in Neural Information Processing Systems, pp.3536-3544, 2014. ,
Robust Object Detection at Regions of Interest with an Application in Ball Recognition, Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp.125-130, 2005. ,
DOI : 10.1109/ROBOT.2005.1570107
Online learning of task-driven object-based visual attention control, Image and Vision Computing, vol.28, issue.7, pp.1130-1145, 2010. ,
DOI : 10.1016/j.imavis.2009.10.006
Reinforcement learning with selective perception and hidden state, 1996. ,
K-nearest neighbor algorithm " , Discovering Knowledge in Data: An Introduction to Data Mining, pp.90-106, 2005. ,
Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words, IEEE Transactions on Robotics, vol.24, issue.5, pp.1027-1037, 2008. ,
DOI : 10.1109/TRO.2008.2004514
URL : https://hal.archives-ouvertes.fr/hal-00652598
On-line Random Forests, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp.1393-1400, 2009. ,
DOI : 10.1109/ICCVW.2009.5457447
Consistency of online random forests, ICML, vol.28, issue.3, pp.1256-1264, 2013. ,
Mondrian forests: Efficient online random forests, Advances in Neural Information Processing Systems, pp.3140-3148, 2014. ,
Object identification from few examples by improving the invariance of a Deep Convolutional Neural Network, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016. ,
DOI : 10.1109/IROS.2016.7759720
Learning depth from single monocular images, Advances in Neural Information Processing Systems, pp.1161-1168, 2005. ,
Active learning of inverse models with intrinsically motivated goal exploration in robots, Robotics and Autonomous Systems, vol.61, issue.1, pp.49-73, 2013. ,
DOI : 10.1016/j.robot.2012.05.008
URL : https://hal.archives-ouvertes.fr/hal-00788440
A Survey on Transfer Learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.10, pp.1345-1359, 2010. ,
DOI : 10.1109/TKDE.2009.191
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.9185
A literature review of domain adaptation with unlabeled data, pp.1-42, 2011. ,
Cnn features off-the-shelf: An astounding baseline for recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.806-813, 2014. ,
Decaf: A deep convolutional activation feature for generic visual recognition, Icml, pp.647-655, 2014. ,
Theory of communication. Part 1: The analysis of information, Journal of the Institution of Electrical Engineers - Part III: Radio and Communication Engineering, vol.93, issue.26, pp.429-441, 1946. ,
DOI : 10.1049/ji-3-2.1946.0074
Make3D: Learning 3D Scene Structure from a Single Still Image, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.5, pp.824-840, 2009. ,
DOI : 10.1109/TPAMI.2008.132
URL : http://ai.stanford.edu/~ang/papers/pami08-make3d.pdf
Very deep convolutional networks for large-scale image recognition, 2014. ,
Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-9, 2015. ,
DOI : 10.1109/CVPR.2015.7298594
URL : http://arxiv.org/abs/1409.4842
Random forests, Machine learning, pp.5-32, 2001. ,
Concentration and dependency ratios, Rivista di politica economica, vol.87, pp.769-792, 1997. ,
Robotique mobile, École Nationale Supérieure de Techniques Avancées, 2011. ,
URL : https://hal.archives-ouvertes.fr/cel-00655005
Incremental topo-metric SLAM using vision and robot odometry, 2011 IEEE International Conference on Robotics and Automation, pp.4067-4073, 2011. ,
DOI : 10.1109/ICRA.2011.5979908
URL : https://hal.archives-ouvertes.fr/hal-00652464
Incremental online topological map building with a mobile robot, Photonics East'99, International Society for Optics and Photonics, pp.129-139, 1999. ,
DOI : 10.1117/12.369248
Topological mapping for mobile robots using a combination of sonar and vision sensing, AAAI, pp.979-984, 1994. ,
The visual homing problem: An example of robotics/biology cross fertilization, Robotics and Autonomous Systems, vol.30, issue.1-2, pp.155-180, 2000. ,
DOI : 10.1016/S0921-8890(99)00070-6
URL : https://hal.archives-ouvertes.fr/hal-00426235
Longterm topological localisation for service robots in dynamic environments using spectral maps, Intelligent Robots and Systems 2014 IEEE/RSJ International Conference on, pp.4537-4542, 2014. ,
Adaptive Mobile Robot Navigation and Mapping, The International Journal of Robotics Research, vol.18, issue.7, pp.650-668, 1999. ,
DOI : 10.1023/A:1007436523611
URL : http://albacore.mit.edu/~jleonard/pubs/ijrr_preprint.pdf
Estimating uncertain spatial relationships in robotics, Proceedings. 1987 IEEE International Conference on Robotics and Automation, pp.167-193, 1990. ,
DOI : 10.1109/ROBOT.1987.1087846
URL : http://arxiv.org/abs/1304.3111
Maintaining representations of the environment of a mobile robot, IEEE Transactions on Robotics and Automation, vol.5, issue.6, pp.804-819, 1989. ,
DOI : 10.1109/70.88101
URL : https://hal.archives-ouvertes.fr/inria-00615531
High resolution maps from wide angle sonar, Proceedings. 1985 IEEE International Conference on Robotics and Automation, pp.116-121, 1985. ,
DOI : 10.1109/ROBOT.1985.1087316
Simultaneous learning of spatial visual attention and physical actions, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.1270-1276, 2010. ,
DOI : 10.1109/IROS.2010.5650749
Simultaneous map building and localization for an autonomous mobile robot " , in Intelligent Robots and Systems' 91.'Intelligence for Mechanical Systems, Proceedings IROS'91. IEEE/RSJ International Workshop on, pp.1442-1447, 1991. ,
Position referencing and consistent world modeling for mobile robots, Proceedings. 1985 IEEE International Conference on Robotics and Automation, pp.138-145, 1985. ,
DOI : 10.1109/ROBOT.1985.1087373
LSD-SLAM: Large-Scale Direct Monocular SLAM, European Conference on Computer Vision, pp.834-849, 2014. ,
DOI : 10.1007/978-3-319-10605-2_54
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.646.7193
3-D Mapping With an RGB-D Camera, IEEE Transactions on Robotics, vol.30, issue.1, pp.177-187, 2014. ,
DOI : 10.1109/TRO.2013.2279412
Introduction to ai robotics, 2000. ,
Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014. ,
DOI : 10.1109/IROS.2014.6942976
Trajectory prediction, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.449-456, 2009. ,
DOI : 10.1145/1553374.1553433
Learning humanoid reaching tasks in dynamic environments, IEEERSJ International Conference on Intelligent Robots and Systems, IEEE, pp.1148-1153, 2007. ,
Probabilistic quadtrees for variable-resolution mapping of large environments, Proceedings of the 5th IFAC/EURON symposium on intelligent autonomous vehicles, 2004. ,
DOI : 10.1016/S1474-6670(17)32056-6
Pose graph optimization with hierarchical conditionally independent graph partitioning, 2016 IEEE International Conference on Intelligent Robots and Systems (IROS), 2016. ,
A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations, Robotics and Autonomous Systems, vol.8, issue.1-2, pp.47-63, 1991. ,
DOI : 10.1016/0921-8890(91)90014-C
Path planning in graph slam using expected uncertainty, 2016 IEEE International Conference on Intelligent Robots and Systems (IROS), 2016. ,
Rapidly-exploring random trees: A new tool for path planning, 1998. ,
A note on two problems in connexion with graphs, Numerische mathematik, pp.269-271, 1959. ,
DOI : 10.1007/BF01386390
A Formal Basis for the Heuristic Determination of Minimum Cost Paths, IEEE Transactions on Systems Science and Cybernetics, vol.4, issue.2, pp.100-107, 1968. ,
DOI : 10.1109/TSSC.1968.300136
Path planning for mobile robot using the particle swarm optimization with mutation operator, Machine Learning and Cybernetics Proceedings of 2004 International Conference on, pp.2473-2478, 2004. ,
Ant colony system algorithm for real-time globally optimal path planning of mobile robots, Acta Automatica Sinica, vol.33, issue.3, pp.279-285, 2007. ,
Github seblague path finding project, https://github ,
Multi-robot systems lab projects, boston university ,
Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998. ,
DOI : 10.1109/TNN.1998.712192
Online planning algorithms for pomdps, Journal of Artificial Intelligence Research, vol.32, pp.663-704, 2008. ,
Despot: Online pomdp planning with regularization, Advances in neural information processing systems, pp.1772-1780, 2013. ,
Exploration???exploitation tradeoff using variance estimates in multi-armed bandits, Theoretical Computer Science, vol.410, issue.19, pp.1876-1902, 2009. ,
DOI : 10.1016/j.tcs.2009.01.016
URL : https://hal.archives-ouvertes.fr/hal-00711069
Spatio-temporal exploration strategies for long-term autonomy of mobile robots, Robotics and Autonomous Systems, vol.88, pp.921-8890, 2016. ,
DOI : 10.1016/j.robot.2016.11.016
Combined Vision and Frontier-Based Exploration Strategies for Semantic Mapping, Informatics in Control, Automation and Robotics, pp.237-244, 2012. ,
DOI : 10.1007/978-3-642-25992-0_34
URL : https://hal.archives-ouvertes.fr/hal-00741266
Planning exploration strategies for simultaneous localization and mapping, Robotics and Autonomous Systems, vol.54, issue.4, pp.314-331, 2006. ,
DOI : 10.1016/j.robot.2005.11.006
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.6861
Planning with movable obstacles in continuous environments with uncertain dynamics, 2013 IEEE International Conference on Robotics and Automation, pp.3832-3838, 2013. ,
DOI : 10.1109/ICRA.2013.6631116
Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Transactions on Evolutionary Computation, vol.1, issue.1, pp.53-66, 1997. ,
DOI : 10.1109/4235.585892
A randomized art-gallery algorithm for sensor placement, Proceedings of the seventeenth annual symposium on Computational geometry , SCG '01, pp.232-240, 2001. ,
DOI : 10.1145/378583.378674
Arc Routing Problems, Part I: The Chinese Postman Problem, Operations Research, vol.43, issue.2, pp.231-242, 1995. ,
DOI : 10.1287/opre.43.2.231
A frontier-based approach for autonomous exploration, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation', pp.146-151, 1997. ,
DOI : 10.1109/CIRA.1997.613851
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.2826
Information gain-based exploration using raoblackwellized particle filters, Robotics: Science and Systems, pp.65-72, 2005. ,
DOI : 10.15607/rss.2005.i.009
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.134.3647
Exploring unknown environments with mobile robots using coverage maps, IJCAI, pp.1127-1134, 2003. ,
A new method for efficient three-dimensional reconstruction of outdoor environments using mobile robots, Journal of Field Robotics, vol.24, issue.11-12, pp.832-853, 2011. ,
DOI : 10.1002/rob.20209
View Planning Problem with Combined View and Traveling Cost, Proceedings 2007 IEEE International Conference on Robotics and Automation, pp.711-716, 2007. ,
DOI : 10.1109/ROBOT.2007.363070
Active 3D scene segmentation and detection of unknown objects, 2010 IEEE International Conference on Robotics and Automation, pp.3114-3120, 2010. ,
DOI : 10.1109/ROBOT.2010.5509973
A Best Next View Selection Algorithm Incorporating a Quality Criterion, Procedings of the British Machine Vision Conference 1998, 1998. ,
DOI : 10.5244/C.12.78
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.7871
Reinforcement learning in robotics: A survey, Reinforcement Learning, pp.579-610, 2012. ,
Reinforcement Learning in Robotics: Applications and Real-World Challenges, Robotics, vol.165, issue.3, pp.122-148, 2013. ,
DOI : 10.1163/156855307781389419
Closed-loop learning of visual control policies, J. Artif. Intell. Res.(JAIR), vol.28, pp.349-391, 2007. ,
Intrinsically motivated reinforcement learning, Advances in neural information processing systems, pp.1281-1288, 2004. ,
Learning visual routines with reinforcement learning, AAAI Fall Symposium, 1996. ,
Reinforcement learning: An introduction, 1998. ,
R-max-a general polynomial time algorithm for nearoptimal reinforcement learning, The Journal of Machine Learning Research, vol.3, pp.213-231, 2003. ,
Near-Bayesian exploration in polynomial time, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.513-520, 2009. ,
DOI : 10.1145/1553374.1553441
Exploration in model-based reinforcement learning by empirically estimating learning progress, Advances in Neural Information Processing Systems, pp.206-214, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00755248
Optimal experiment design, Measurement Science and Technology, vol.9, issue.6, p.864, 1998. ,
DOI : 10.1088/0957-0233/9/6/003
Predictive exploration considering previously mapped environments, 2015 IEEE International Conference on Robotics and Automation (ICRA), pp.2761-2766, 2015. ,
DOI : 10.1109/ICRA.2015.7139574
An experimental comparison between NMF and LDA for active cross-situational object-word learning, 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2016. ,
DOI : 10.1109/DEVLRN.2016.7846822
URL : https://hal.archives-ouvertes.fr/hal-01370853
Scene recognition and weakly supervised object localization with deformable part-based models, 2011 International Conference on Computer Vision, pp.1307-1314, 2011. ,
DOI : 10.1109/ICCV.2011.6126383
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.300.7841
Curious model-building control systems, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks, pp.1458-1463, 1991. ,
DOI : 10.1109/IJCNN.1991.170605
The zone of proximal development in vygotsky's analysis of learning and instruction " , Vygotsky's educational theory in cultural context, pp.39-64, 2003. ,
A baby robot platform for cognitive developmental robotics, Proceedings of the IEEE, 2009. ,
Poppy: Plate-forme robotique open source, imprimée en 3d et totalement modulaire pour l'experimentation scientifique, artistique et pédagogique, 2015. ,
Achieving artificial intelligence through building robots, DTIC Document, Tech. Rep, 1986. ,
R-IAC: Robust Intrinsically Motivated Exploration and Active Learning, Autonomous Mental Development, pp.155-169, 2009. ,
DOI : 10.1109/TAMD.2009.2037513
Overlapping waves in tool use development: A curiosity-driven computational model, 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2016. ,
DOI : 10.1109/DEVLRN.2016.7846825
URL : https://hal.archives-ouvertes.fr/hal-01384562
Exploration strategies in developmental robotics: A unified probabilistic framework, 2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL), pp.1-6, 2013. ,
DOI : 10.1109/DevLrn.2013.6652535
URL : https://hal.archives-ouvertes.fr/hal-00860641
Autonomous learning of abstractions using Curiosity-Driven Modular Incremental Slow Feature Analysis, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), pp.2012-2013 ,
DOI : 10.1109/DevLrn.2012.6400829
An iterative algorithm for forward-parameterized skill discovery, 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2016. ,
DOI : 10.1109/DEVLRN.2016.7846816
URL : https://hal.archives-ouvertes.fr/hal-01370820
The interaction of maturational constraints and intrinsic motivations in active motor development, 2011 IEEE International Conference on Development and Learning (ICDL), pp.1-8, 2011. ,
DOI : 10.1109/DEVLRN.2011.6037315
URL : https://hal.archives-ouvertes.fr/hal-00646585
Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions, Contemporary Educational Psychology, vol.25, issue.1, pp.54-67, 2000. ,
DOI : 10.1006/ceps.1999.1020
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.318.808
A Neural Substrate of Prediction and Reward, Science, vol.263, issue.5149, pp.1593-1599, 1997. ,
DOI : 10.1126/science.7508638
Reward, Motivation, and Reinforcement Learning, Neuron, vol.36, issue.2, pp.285-298, 2002. ,
DOI : 10.1016/S0896-6273(02)00963-7
URL : http://doi.org/10.1016/s0896-6273(02)00963-7
Novelty and reinforcement learning in the value system of developmental robots, 2002. ,
Visual adaptation and novelty responses in the superior colliculus, European Journal of Neuroscience, vol.8, issue.5, pp.766-779, 2011. ,
DOI : 10.1097/00001756-199705260-00035
Credit Assignment in Multiple Goal Embodied Visuomotor Behavior, Frontiers in Psychology, vol.1, p.217, 2010. ,
DOI : 10.3389/fpsyg.2010.00173
URL : http://doi.org/10.3389/fpsyg.2010.00173
Confidence-based progress-driven selfgenerated goals for skill acquisition in developmental robots, 2013. ,
Curious agents and situated design evaluations, AI EDAM, vol.18, issue.02, pp.153-161, 2004. ,
DOI : 10.1145/37402.37406
A computational model of achievement motivation for artificial agents International Foundation for Autonomous Agents and Multiagent Systems, The 10th International Conference on Autonomous Agents and Multiagent Systems, pp.1067-1068, 2011. ,
Diversity-driven selection of exploration strategies in multiarmed bandits, 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics, pp.135-142, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01251060
Motivational principles for visual know-how development, 2003. ,
How can we define intrinsic motivation?, proceedings of the 8th international conference on epigenetic robotics: modeling cognitive development in robotic systems, 2008. ,
URL : https://hal.archives-ouvertes.fr/inria-00420175
Value systems for developmental cognitive robotics: A survey, Cognitive Systems Research, vol.41, pp.38-55, 2017. ,
DOI : 10.1016/j.cogsys.2016.08.001
In search of the neural circuits of intrinsic motivation, Frontiers in Neuroscience, vol.1, issue.1, p.225, 2007. ,
DOI : 10.3389/neuro.01.1.1.017.2007
Information-seeking, curiosity, and attention: computational and neural mechanisms, Trends in Cognitive Sciences, vol.17, issue.11, pp.585-593, 2013. ,
DOI : 10.1016/j.tics.2013.09.001
URL : https://hal.archives-ouvertes.fr/hal-00913646
Eye movements reveal epistemic curiosity in human observers, Vision Research, vol.117, pp.81-90, 2015. ,
DOI : 10.1016/j.visres.2015.10.009
URL : https://hal.archives-ouvertes.fr/hal-01250727
Eye-Hand Coordination during Learning of a Novel Visuomotor Task, Journal of Neuroscience, vol.25, issue.39, pp.8833-8842, 2005. ,
DOI : 10.1523/JNEUROSCI.2658-05.2005
Explore to see, learn to perceive, get the actions for free: SKILLABILITY, 2014 International Joint Conference on Neural Networks (IJCNN), pp.2705-2712, 2014. ,
DOI : 10.1109/IJCNN.2014.6889784
Intrinsically motivated learning of visual motion perception and smooth pursuit, 2014 IEEE International Conference on Robotics and Automation (ICRA), pp.1902-1908, 2014. ,
DOI : 10.1109/ICRA.2014.6907110
URL : http://arxiv.org/abs/1402.3344
Integration of Active Vision and Reaching From a Developmental Robotics Perspective, IEEE Transactions on Autonomous Mental Development, vol.2, issue.4, pp.355-367, 2010. ,
DOI : 10.1109/TAMD.2010.2081667
ICAT: a computational model for the adaptive control of fixation durations, Psychonomic Bulletin & Review, vol.36, issue.3, pp.907-934, 2014. ,
DOI : 10.3758/BF03206369
Computational model for predicting visual fixations from childhood to adulthood, 2017. ,
Cultural Differences in Allocation of Attention in Visual Information Processing, Journal of Cross-Cultural Psychology, vol.26, issue.1, pp.349-360, 2009. ,
DOI : 10.1080/036107300243588
The strategic student approach for life-long exploration and learning, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), pp.1-8, 2012. ,
DOI : 10.1109/DevLrn.2012.6400807
URL : https://hal.archives-ouvertes.fr/hal-00755216
Artificial curiosity with planning for autonomous perceptual and cognitive development, 2011 IEEE International Conference on Development and Learning (ICDL), pp.1-8, 2011. ,
DOI : 10.1109/DEVLRN.2011.6037356
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.300.1823
The playground experiment: Taskindependent development of a curious robot, Proceedings of the AAAI Spring Symposium on Developmental Robotics, pp.42-47, 2005. ,
Active learning of local predictable representations with artificial curiosity, 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp.228-233, 2015. ,
DOI : 10.1109/DEVLRN.2015.7346145
URL : https://hal.archives-ouvertes.fr/hal-01205619
Online Goal Babbling for rapid bootstrapping of inverse models in high dimensions, 2011 IEEE International Conference on Development and Learning (ICDL), pp.1-8, 2011. ,
DOI : 10.1109/DEVLRN.2011.6037368
Least-squares policy iteration, Journal of machine learning research, vol.4, pp.1107-1149, 2003. ,
Optimal control-1950 to 1985, IEEE Control Systems Magazine, vol.16, issue.3, pp.26-33, 1996. ,
DOI : 10.1109/37.506395
Q-learning, Machine learning, vol.8, issue.3-4, pp.279-292, 1992. ,
Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation, Advances in Neural Information Processing Systems, pp.3675-3683, 2016. ,
Active learning in multimedia annotation and retrieval, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.2, p.10, 2011. ,
DOI : 10.1145/1899412.1899414