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Recognizing environments from action sequences using self-organizing maps

S. Yamada
2004 Applied Soft Computing  
Then the sequences of actions executed in each room are transformed into environment vectors for self-organizing maps.  ...  In this paper, we describe development of a mobile robot which does unsupervised learning for recognizing an environment from action sequences.  ...  Environment recognition using SOM SOM: self-organizing maps In this section, we briefly explain Kohonen's SOM [18] .  ... 
doi:10.1016/j.asoc.2003.07.001 fatcat:fjlu4w2pobcbzb7sc3dvgsbjuy

Recognizing Environments from Action Sequences Using a Self-Organizing Map

Seiji Yamada, Morimichi Murota
1999 Journal of the Robotics Society of Japan  
Then the sequences of actions executed in each enclosure are transformed into input vectors for a self-organizing map.  ...  In this paper, we describe development of a mobile robot which does unsupervised learning for recognizing environments from action sequences.  ...  map Fig. 9 9 An Fig. 10 10 Seven rooms Fig.12 Fig.12(a) 2 Fig.12(b) 2 Fig. 12 Environments with obstacles Table 1 1 Experimental results on 20 rooms Initial weights Sape: line Sape: circle  ... 
doi:10.7210/jrsj.17.855 fatcat:wbg4grs23jhyveym2v55sfbqha

Learning behaviors for environmental modeling by genetic algorithm [chapter]

Seiji Yamada
1998 Lecture Notes in Computer Science  
We have proposed AEM (Action-based Environment Modeling) which is an appropriate approach for a simple mobile robot to recognize environments, and made experiments using a real robot.  ...  Thus we propose the evolutionary design method of such behaviors using genetic algorithm and make experiments in which a robot recognizes the environments with different structures.  ...  Nehmzow and Smithers studied on recognizing corners in simple enclosures with a self-organizing network [12] .  ... 
doi:10.1007/3-540-64957-3_72 fatcat:b3n2ozacbfdknbk3gpmp3bhclu

Application of Self-Organizing Map for analyzing robotic arm�s action with Consciousness-Based Architecture module

Wisanu Jitviriya, Eiji Hayashi
2014 Journal of Robotics, Networking and Artificial Life (JRNAL)  
Furthermore, the robot should select the action itself, we have investigated the application of brain-inspired technology so we introduced a Self-Organizing Map (SOM) neural network that is trained using  ...  In this paper, we attempt to describe the integration of a Self-Organizing Map (SOM) method into a CBA module in order to classify and select autonomous behavior.  ...  using a Self-Organizing Map method with CBA module In this section, we are aiming for an integrated development the autonomous behavior system that combines a sample the Self-Organizing Map and CBA module  ... 
doi:10.2991/jrnal.2014.1.2.8 fatcat:6zr3esve3bdiniql7uqdgj4274

Human Action Recognition and Assessment via Deep Neural Network Self-Organization [article]

German I. Parisi
2020 arXiv   pre-print
In this chapter, I introduce a set of hierarchical models for the learning and recognition of actions from depth maps and RGB images through the use of neural network self-organization.  ...  A particularity of these models is the use of growing self-organizing networks that quickly adapt to non-stationary distributions and implement dedicated mechanisms for continual learning from temporally  ...  These models use different variants of growing self-organizing networks for the learning of action sequences and real-time inference.  ... 
arXiv:2001.05837v2 fatcat:ct2c5aevrffuzewy5mgj5jhaiq

Evolutionary behavior learning for action-based environment modeling by a mobile robot

S. Yamada
2005 Applied Soft Computing  
In AEM, a behavior-based mobile robot acts in each environment and action sequences are obtained.  ...  We have proposed Action-based Environment Modeling (AEM) approach for a simple mobile robot to recognize environments.  ...  Nehmzow and Smithers studied on recognizing corners in simple enclosures with a self-organizing network [11] .  ... 
doi:10.1016/j.asoc.2004.07.004 fatcat:elcr5yz6ejglnk35svuuwjmdna

Emergence of multimodal action representations from neural network self-organization

German I. Parisi, Jun Tani, Cornelius Weber, Stefan Wermter
2017 Cognitive Systems Research  
Multimodal representations of actions are obtained using the co-activation of action features from video sequences and labels from automatic speech recognition.  ...  We propose a hierarchical architecture with growing self-organizing neural networks for learning human actions from audiovisual inputs.  ...  Emergence of multimodal action representations from neural network self-organization.  ... 
doi:10.1016/j.cogsys.2016.08.002 fatcat:uvsy7swgkvgypnudisbilpuz2i

Recognition of Transitive Actions with Hierarchical Neural Network Learning [chapter]

Luiza Mici, German I. Parisi, Stefan Wermter
2016 Lecture Notes in Computer Science  
We process separately body poses extracted from depth map sequences and object features from RGB images.  ...  In this paper, we present a hierarchical selforganizing neural architecture for learning to recognize transitive actions from RGB-D videos.  ...  In this paper, we present a hierarchical, self-organizing neural architecture that learns to recognize transitive actions from RGB-D videos containing daily activities.  ... 
doi:10.1007/978-3-319-44781-0_56 fatcat:z4hsq7v6kbea5o2ozszqerosry

Dense RGB-D Map-Based Human Tracking and Activity Recognition using Skin Joints Features and Self-Organizing Map

2015 KSII Transactions on Internet and Information Systems  
Lastly, self-organized maps are used to recognize different activities.  ...  This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework.  ...  self-organized map (SOM).  ... 
doi:10.3837/tiis.2015.05.017 fatcat:lopculvkwfcfvocamxz4ty6aqe

The development of spatial recognition and navigation in hierarchical recurrent neural network with convolution processing

Wataru Noguchi, Hiroyuki Iizuka, Masahito Yamamoto
2017 Proceedings of the 14th European Conference on Artificial Life ECAL 2017  
These results confirmed that the PoM task was a major factor in the self-organization of the cognitive map.  ...  the HRNN organize the cognitive map.  ... 
doi:10.7551/ecal_a_055 dblp:conf/ecal/NoguchiIY17 fatcat:2njbqoxfargobj3v4nc5637f3q

Recognizing actions with the associative self-organizing map

Miriam Buonamente, Haris Dindo, Magnus Johnsson
2013 2013 XXIV International Conference on Information, Communication and Automation Technologies (ICAT)  
The method we propose is based on the Associative Self Organizing Map (A-SOM), a variant of the Self Organizing Map.  ...  Once the A-SOM has learnt to recognize actions, it uses this learning to predict the continuation of an observed initial movement of an agent, in this way reading its intentions.  ...  The study presented here is part of a bigger project whose first step was to efficiently represent and recognize human actions [14] by using the Associative Self-Organizing Map (A-SOM) [15] .  ... 
doi:10.1109/icat.2013.6684076 dblp:conf/icatech/BuonamenteDJ13 fatcat:ehjofvq3pzexlie3mxmpoz26la

Facial Expression Recognition from Video Sequence Using Self Organizing Feature Map

Walid Amin Mahmoud, Jane Jaleel Stephan, Anmar Abdel Wahab Razzak Razzak
2021 Journal Port Science Research  
The approach utilizes the topological ordering patterns produced by Kohonen Self Organizing Map, in which implemented on expression image sequence for each prototype facial expression.  ...  The map will compute the topological relationship between the particular expression sequences, starting from the neutral expression to peak.  ...  SELF-ORGANIZING MAPS (SOM) The Self-Organizing Feature Map (SOFM) was introduced by Teuvo Kohonen [7, 8] .  ... 
doi:10.36371/port.2020.2.2 fatcat:ehgpwhzbnzdchfrxu3szhusvty

A self-organizing neural network architecture for learning human-object interactions [article]

Luiza Mici, German I. Parisi, Stefan Wermter
2018 arXiv   pre-print
In this paper, we present a self-organizing neural network for the recognition of human-object interactions from RGB-D videos.  ...  In line with neurophysiological studies, our self-organizing architecture exhibits higher neural activation for congruent action-object pairs learned during training sessions with respect to synthetically  ...  In this paper, we present a self-organizing neural architecture that learns to recognize human-object interactions from RGB-D videos.  ... 
arXiv:1710.01916v2 fatcat:eu7c7wn3anfx5hjzabufbrrdvq

Action in Mind: A Neural Network Approach to Action Recognition and Segmentation [article]

Zahra Gharaee
2021 arXiv   pre-print
It utilizes self-organizing neural networks such as Kohonen feature maps and growing grids as the main neural network layers.  ...  The experimental results of different system level developments show that the system performs well with quite high accuracy for recognizing human actions.  ...  Using self-organizing neural networks such as self-organizing maps or growing grids performs the first and the second tasks.  ... 
arXiv:2104.14870v1 fatcat:cp7gnx34jbdq5o45zbfmhlrhlm

Bootstrap learning of foundational representations

Benjamin J. Kuipers, Patrick Beeson, Joseph Modayil, Jefferson Provost
2006 Connection science  
Building on this framework, we show how an agent can use self-organizing maps to identify useful sensory featurs in the environment, and can learn effective hill-climbing control laws to define distinctive  ...  and from each other, and can learn properties useful for classification.  ...  Research of the Intelligent Robotics lab is supported in part by grants from the National Science Foundation (IIS-0413257 and IIS-0538927), from the National Institutes of Health (EY016089), and by an  ... 
doi:10.1080/09540090600768484 fatcat:ckqjttktnvdbtb4ekftx3q22gq
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