Pub Date : 2013-11-04DOI: 10.1109/DEVLRN.2013.6652552
Severin Fichtl, J. W. Alexander, Frank Guerin, Wail Mustafa, D. Kraft, N. Krüger
In this work, we learn a limited number of abstractions which can then be used to form preconditions for motor actions. These abstractions take the form of spatial relations amongst objects. We consider three “classes” of spatial relation: The objects either are separated from, on-top of, or inside each other. We have tackled this same problem in previous work (Fichtl et al., 2013). Here we report on recent improved results using a novel application of histograms to visually recognise a spatial relation between objects in the environment. Using this histogram based approach we are able to report a very high rate of success when the system is asked to recognise a spatial relation.
在这项工作中，我们学习了有限数量的抽象概念，然后可以用来形成运动动作的先决条件。这些抽象采用对象之间空间关系的形式。我们考虑空间关系的三“类”:对象要么是彼此分离的，要么是彼此之上的，要么是彼此内部的。我们在之前的工作中解决了同样的问题(Fichtl et al.， 2013)。在这里，我们报告了最近使用直方图的新应用来视觉识别环境中物体之间的空间关系的改进结果。使用这种基于直方图的方法，当系统被要求识别空间关系时，我们能够报告非常高的成功率。
{"title":"Learning spatial relations between objects from 3D scenes","authors":"Severin Fichtl, J. W. Alexander, Frank Guerin, Wail Mustafa, D. Kraft, N. Krüger","doi":"10.1109/DEVLRN.2013.6652552","DOIUrl":"https://doi.org/10.1109/DEVLRN.2013.6652552","url":null,"abstract":"In this work, we learn a limited number of abstractions which can then be used to form preconditions for motor actions. These abstractions take the form of spatial relations amongst objects. We consider three “classes” of spatial relation: The objects either are separated from, on-top of, or inside each other. We have tackled this same problem in previous work (Fichtl et al., 2013). Here we report on recent improved results using a novel application of histograms to visually recognise a spatial relation between objects in the environment. Using this histogram based approach we are able to report a very high rate of success when the system is asked to recognise a spatial relation.","PeriodicalId":106997,"journal":{"name":"2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128481259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-11-04DOI: 10.1109/DEVLRN.2013.6652564
A. D. Rengervé, Souheil Hanoune, P. Andry, M. Quoy, P. Gaussier
Trajectories can be encoded as attraction basin resulting from recruited associations between visually based localization and orientations to follow (low level behaviors). Navigation to different places according to some other multimodal information needs a particular learning. We propose a minimal model explaining such a behavior adaptation from non-verbal interaction with a teacher. Specific contexts can be recruited to prevent the behaviors to activate in cases the interaction showed they were inadequate. Still, the model is compatible with the recruitment of new low level behaviors. The tests done in simulation show the capabilities of the architecture, the limitations regarding the generalization and the learning speed. We also discuss the possible evolutions towards more bio-inspired models.
{"title":"Building specific contexts for on-line learning of dynamical tasks through non-verbal interaction","authors":"A. D. Rengervé, Souheil Hanoune, P. Andry, M. Quoy, P. Gaussier","doi":"10.1109/DEVLRN.2013.6652564","DOIUrl":"https://doi.org/10.1109/DEVLRN.2013.6652564","url":null,"abstract":"Trajectories can be encoded as attraction basin resulting from recruited associations between visually based localization and orientations to follow (low level behaviors). Navigation to different places according to some other multimodal information needs a particular learning. We propose a minimal model explaining such a behavior adaptation from non-verbal interaction with a teacher. Specific contexts can be recruited to prevent the behaviors to activate in cases the interaction showed they were inadequate. Still, the model is compatible with the recruitment of new low level behaviors. The tests done in simulation show the capabilities of the architecture, the limitations regarding the generalization and the learning speed. We also discuss the possible evolutions towards more bio-inspired models.","PeriodicalId":106997,"journal":{"name":"2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126673079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-11-04DOI: 10.1109/DEVLRN.2013.6652556
Joseph M. Burling, Hanako Yoshida, Y. Nagai
Before babies acquire an adult-like visual capacity, they participate in a social world as a human learning system which promotes social activities around them and in turn dramatically alters their own social participation. Visual input becomes more dynamic as they gain self-generated movement, and such movement has a potential role in learning. The present study specifically looks at the expected change in motion of the early visual input that infants are exposed to, and the corresponding attentional coordination within the specific context of parent-infant interactions. The results will be discussed in terms of the significance of social input for development.
{"title":"The significance of social input, early motion experiences, and attentional selection","authors":"Joseph M. Burling, Hanako Yoshida, Y. Nagai","doi":"10.1109/DEVLRN.2013.6652556","DOIUrl":"https://doi.org/10.1109/DEVLRN.2013.6652556","url":null,"abstract":"Before babies acquire an adult-like visual capacity, they participate in a social world as a human learning system which promotes social activities around them and in turn dramatically alters their own social participation. Visual input becomes more dynamic as they gain self-generated movement, and such movement has a potential role in learning. The present study specifically looks at the expected change in motion of the early visual input that infants are exposed to, and the corresponding attentional coordination within the specific context of parent-infant interactions. The results will be discussed in terms of the significance of social input for development.","PeriodicalId":106997,"journal":{"name":"2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121747729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-18DOI: 10.1109/DEVLRN.2013.6652563
Olivier Mangin, Pierre-Yves Oudeyer
Perceptual systems often include sensors from several modalities. However, existing robots do not yet sufficiently discover patterns that are spread over the flow of multimodal data they receive. In this paper we present a framework that learns a dictionary of words from full spoken utterances, together with a set of gestures from human demonstrations and the semantic connection between words and gestures. We explain how to use a nonnegative matrix factorization algorithm to learn a dictionary of components that represent meaningful elements present in the multimodal perception, without providing the system with a symbolic representation of the semantics. We illustrate this framework by showing how a learner discovers word-like components from observation of gestures made by a human together with spoken descriptions of the gestures, and how it captures the semantic association between the two.
{"title":"Learning semantic components from subsymbolic multimodal perception","authors":"Olivier Mangin, Pierre-Yves Oudeyer","doi":"10.1109/DEVLRN.2013.6652563","DOIUrl":"https://doi.org/10.1109/DEVLRN.2013.6652563","url":null,"abstract":"Perceptual systems often include sensors from several modalities. However, existing robots do not yet sufficiently discover patterns that are spread over the flow of multimodal data they receive. In this paper we present a framework that learns a dictionary of words from full spoken utterances, together with a set of gestures from human demonstrations and the semantic connection between words and gestures. We explain how to use a nonnegative matrix factorization algorithm to learn a dictionary of components that represent meaningful elements present in the multimodal perception, without providing the system with a symbolic representation of the semantics. We illustrate this framework by showing how a learner discovers word-like components from observation of gestures made by a human together with spoken descriptions of the gestures, and how it captures the semantic association between the two.","PeriodicalId":106997,"journal":{"name":"2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL)","volume":"19 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130033685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-18DOI: 10.1109/DEVLRN.2013.6652567
Fabien C. Y. Benureau, Pierre-Yves Oudeyer
We present an algorithm for transferring exploration strategies between tasks that share a common motor space in the context of lifelong autonomous learning in robotics. The algorithm does not transfer observations, or make assumptions about how the learning is conducted. Instead, only selected motor commands are transferred between tasks, chosen autonomously according to an empirical measure of learning progress. We show that on a wide variety of variations from a source task, such as changing the object the robot is interacting with or altering the morphology of the robot, this simple and flexible transfer method increases early performance significantly in the new task. We also provide examples of situations where the transfer is not helpful.
{"title":"Autonomous reuse of motor exploration trajectories","authors":"Fabien C. Y. Benureau, Pierre-Yves Oudeyer","doi":"10.1109/DEVLRN.2013.6652567","DOIUrl":"https://doi.org/10.1109/DEVLRN.2013.6652567","url":null,"abstract":"We present an algorithm for transferring exploration strategies between tasks that share a common motor space in the context of lifelong autonomous learning in robotics. The algorithm does not transfer observations, or make assumptions about how the learning is conducted. Instead, only selected motor commands are transferred between tasks, chosen autonomously according to an empirical measure of learning progress. We show that on a wide variety of variations from a source task, such as changing the object the robot is interacting with or altering the morphology of the robot, this simple and flexible transfer method increases early performance significantly in the new task. We also provide examples of situations where the transfer is not helpful.","PeriodicalId":106997,"journal":{"name":"2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116156700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/DEVLRN.2013.6652560
C. Dawson, Jeremy B. Wright, Antons Rebguns, M. Valenzuela-Escarcega, Daniel Fried, P. Cohen
The language of space and spatial relations is a rich source of abstract semantic structure. We develop a probabilistic model that learns to understand utterances that describe spatial configurations of objects in a tabletop scene by seeking the meaning that best explains the sentence chosen. The inference problem is simplified by assuming that sentences express symbolic representations of (latent) semantic relations between referents and landmarks in space, and that given these symbolic representations, utterances and physical locations are conditionally independent. As such, the inference problem factors into a symbol-grounding component (linking propositions to physical locations) and a symbol-translation component (linking propositions to parse trees). We evaluate the model by eliciting production and comprehension data from human English speakers and find that our system recovers the referent of spatial utterances at a level of proficiency approaching human performance.
{"title":"A generative probabilistic framework for learning spatial language","authors":"C. Dawson, Jeremy B. Wright, Antons Rebguns, M. Valenzuela-Escarcega, Daniel Fried, P. Cohen","doi":"10.1109/DEVLRN.2013.6652560","DOIUrl":"https://doi.org/10.1109/DEVLRN.2013.6652560","url":null,"abstract":"The language of space and spatial relations is a rich source of abstract semantic structure. We develop a probabilistic model that learns to understand utterances that describe spatial configurations of objects in a tabletop scene by seeking the meaning that best explains the sentence chosen. The inference problem is simplified by assuming that sentences express symbolic representations of (latent) semantic relations between referents and landmarks in space, and that given these symbolic representations, utterances and physical locations are conditionally independent. As such, the inference problem factors into a symbol-grounding component (linking propositions to physical locations) and a symbol-translation component (linking propositions to parse trees). We evaluate the model by eliciting production and comprehension data from human English speakers and find that our system recovers the referent of spatial utterances at a level of proficiency approaching human performance.","PeriodicalId":106997,"journal":{"name":"2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132583270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/DEVLRN.2013.6652551
M. Ogino, Junji Suzuki, M. Asada
Human feels three-dimensional effect for static image with the cues of various kinds of image features such as relative sizes of objects, up and down, rules of perspective, texture gradient, and shadow. The features are called pictorial depth cues. Human is thought to learn to extract these features as important cues for depth estimation in the developmental process. In this paper, we make a hypothesis that pictorial depth cues are acquired so that disparities can be predicted well and make a model that extracts features appropriate for depth estimation from static images. Random forest network is trained to extract important ones among a large amount image features so as to estimate motion and stereo disparities. The experiments with simulation and real environments show high correlation between estimated and real disparities.
{"title":"Extracting image features in static images for depth estimation","authors":"M. Ogino, Junji Suzuki, M. Asada","doi":"10.1109/DEVLRN.2013.6652551","DOIUrl":"https://doi.org/10.1109/DEVLRN.2013.6652551","url":null,"abstract":"Human feels three-dimensional effect for static image with the cues of various kinds of image features such as relative sizes of objects, up and down, rules of perspective, texture gradient, and shadow. The features are called pictorial depth cues. Human is thought to learn to extract these features as important cues for depth estimation in the developmental process. In this paper, we make a hypothesis that pictorial depth cues are acquired so that disparities can be predicted well and make a model that extracts features appropriate for depth estimation from static images. Random forest network is trained to extract important ones among a large amount image features so as to estimate motion and stereo disparities. The experiments with simulation and real environments show high correlation between estimated and real disparities.","PeriodicalId":106997,"journal":{"name":"2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126726676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/DEVLRN.2013.6652565
M. Peniak, Anthony F. Morse, A. Cangelosi
The modelling of the integration of various cognitive skills and modalities requires complex and computationally intensive algorithms running in parallel while controlling high-performance systems. The distribution of processing across many computers has certainly advanced our software ecosystem and opened up research to new possibilities. While this was an essential move, we are aspiring to augment the field of cognitive robotics by providing Aquila 2.0, a novel hi-performance software architecture utilising cross-platform, heterogeneous CPU-GPU modules loosely coupled with GUIs used for module management and data visualisation.
{"title":"Aquila 2.0 software architecture for cognitive robotics","authors":"M. Peniak, Anthony F. Morse, A. Cangelosi","doi":"10.1109/DEVLRN.2013.6652565","DOIUrl":"https://doi.org/10.1109/DEVLRN.2013.6652565","url":null,"abstract":"The modelling of the integration of various cognitive skills and modalities requires complex and computationally intensive algorithms running in parallel while controlling high-performance systems. The distribution of processing across many computers has certainly advanced our software ecosystem and opened up research to new possibilities. While this was an essential move, we are aspiring to augment the field of cognitive robotics by providing Aquila 2.0, a novel hi-performance software architecture utilising cross-platform, heterogeneous CPU-GPU modules loosely coupled with GUIs used for module management and data visualisation.","PeriodicalId":106997,"journal":{"name":"2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123169777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/DEVLRN.2013.6652559
K. Shibata, Kenta Goto
In this paper, the authors first point the importance of three factors for filling the gap between humans and robots in the flexibility in the real world. Those are (1)parallel processing, (2)emergence through learning and solving “what” problems, and (3)abstraction and generalization on the abstract space. To explore the possibility of human-like flexibility in robots, a prediction-required task in which an agent (robot) gets a reward by capturing a moving target that sometimes becomes invisible was learned by reinforcement learning using a recurrent neural network. Even though the agent did not know in advance that “prediction is required” or “what information should be predicted”, appropriate discrete decision making, in which `capture' or `move' was chosen, and also continuous motion generation in two-dimensional space, could be acquired. Furthermore, in this task, the target sometimes changed its moving direction randomly when it became visible again from invisible state. Then the agent could change its moving direction promptly and appropriately without introducing any special architecture or technique. Such emergent property is what general parallel processing systems such as Subsumption architecture do not have, and the authors believe it is a key to solve the “Frame Problem” fundamentally.
{"title":"Emergence of flexible prediction-based discrete decision making and continuous motion generation through actor-Q-learning","authors":"K. Shibata, Kenta Goto","doi":"10.1109/DEVLRN.2013.6652559","DOIUrl":"https://doi.org/10.1109/DEVLRN.2013.6652559","url":null,"abstract":"In this paper, the authors first point the importance of three factors for filling the gap between humans and robots in the flexibility in the real world. Those are (1)parallel processing, (2)emergence through learning and solving “what” problems, and (3)abstraction and generalization on the abstract space. To explore the possibility of human-like flexibility in robots, a prediction-required task in which an agent (robot) gets a reward by capturing a moving target that sometimes becomes invisible was learned by reinforcement learning using a recurrent neural network. Even though the agent did not know in advance that “prediction is required” or “what information should be predicted”, appropriate discrete decision making, in which `capture' or `move' was chosen, and also continuous motion generation in two-dimensional space, could be acquired. Furthermore, in this task, the target sometimes changed its moving direction randomly when it became visible again from invisible state. Then the agent could change its moving direction promptly and appropriately without introducing any special architecture or technique. Such emergent property is what general parallel processing systems such as Subsumption architecture do not have, and the authors believe it is a key to solve the “Frame Problem” fundamentally.","PeriodicalId":106997,"journal":{"name":"2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127952319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/DEVLRN.2013.6652525
S. Nguyen, S. Ivaldi, Natalia Lyubova, Alain Droniou, Damien Gérardeaux-Viret, David Filliat, V. Padois, Olivier Sigaud, Pierre-Yves Oudeyer
In this paper we address the problem of learning to recognize objects by manipulation in a developmental robotics scenario. In a life-long learning perspective, a humanoid robot should be capable of improving its knowledge of objects with active perception. Our approach stems from the cognitive development of infants, exploiting active curiosity-driven manipulation to improve perceptual learning of objects. These functionalities are implemented as perception, control and active exploration modules as part of the Cognitive Architecture of the MACSi project. In this paper we integrate these functionalities into an active perception system which learns to recognise objects through manipulation. Our work in this paper integrates a bottom-up vision system, a control system of a complex robot system and a top-down interactive exploration method, which actively chooses an exploration method to collect data and whether interacting with humans is profitable or not. Experimental results show that the humanoid robot iCub can learn to recognize 3D objects by manipulation and in interaction with teachers by choosing the adequate exploration strategy to enhance competence progress and by focusing its efforts on the most complex tasks. Thus the learner can learn interactively with humans by actively self-regulating its requests for help.
{"title":"Learning to recognize objects through curiosity-driven manipulation with the iCub humanoid robot","authors":"S. Nguyen, S. Ivaldi, Natalia Lyubova, Alain Droniou, Damien Gérardeaux-Viret, David Filliat, V. Padois, Olivier Sigaud, Pierre-Yves Oudeyer","doi":"10.1109/DEVLRN.2013.6652525","DOIUrl":"https://doi.org/10.1109/DEVLRN.2013.6652525","url":null,"abstract":"In this paper we address the problem of learning to recognize objects by manipulation in a developmental robotics scenario. In a life-long learning perspective, a humanoid robot should be capable of improving its knowledge of objects with active perception. Our approach stems from the cognitive development of infants, exploiting active curiosity-driven manipulation to improve perceptual learning of objects. These functionalities are implemented as perception, control and active exploration modules as part of the Cognitive Architecture of the MACSi project. In this paper we integrate these functionalities into an active perception system which learns to recognise objects through manipulation. Our work in this paper integrates a bottom-up vision system, a control system of a complex robot system and a top-down interactive exploration method, which actively chooses an exploration method to collect data and whether interacting with humans is profitable or not. Experimental results show that the humanoid robot iCub can learn to recognize 3D objects by manipulation and in interaction with teachers by choosing the adequate exploration strategy to enhance competence progress and by focusing its efforts on the most complex tasks. Thus the learner can learn interactively with humans by actively self-regulating its requests for help.","PeriodicalId":106997,"journal":{"name":"2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL)","volume":"2 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131943364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：