Online Exploratory Behavior Acquisition of Mobile Robot Based on Reinforcement Learning

Gouko, Manabu; Kobayashi, Yuichi; Kim, Chyon Hae

doi:10.1007/978-3-642-38577-3_28

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…In addition, various studies on active perception by a robot have been conducted (Natale et al, 2004 ; Ji and Carin, 2006 ; Schneider et al, 2009 ; Tuci et al, 2010 ; Saegusa et al, 2011 ; Fishel and Loeb, 2012 ; Pape et al, 2012 ; Sushkov and Sammut, 2012 ; Gouko et al, 2013 ; Hogman et al, 2013 ; Ivaldi et al, 2014 ; Zhang et al, 2017 ). In spite of a large number of contributions about active perception, few theories of active perception for multimodal object category recognition have been proposed.…”

Section: Background and Related Workmentioning

confidence: 99%

Multimodal Hierarchical Dirichlet Process-Based Active Perception by a Robot

2018

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In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object. We propose an active perception for MHDP method that uses the information gain (IG) maximization criterion and lazy greedy algorithm. We show that the IG maximization criterion is optimal in the sense that the criterion is equivalent to a minimization of the expected Kullback–Leibler divergence between a final recognition state and the recognition state after the next set of actions. However, a straightforward calculation of IG is practically impossible. Therefore, we derive a Monte Carlo approximation method for IG by making use of a property of the MHDP. We also show that the IG has submodular and non-decreasing properties as a set function because of the structure of the graphical model of the MHDP. Therefore, the IG maximization problem is reduced to a submodular maximization problem. This means that greedy and lazy greedy algorithms are effective and have a theoretical justification for their performance. We conducted an experiment using an upper-torso humanoid robot and a second one using synthetic data. The experimental results show that the method enables the robot to select a set of actions that allow it to recognize target objects quickly and accurately. The numerical experiment using the synthetic data shows that the proposed method can work appropriately even when the number of actions is large and a set of target objects involves objects categorized into multiple classes. The results support our theoretical outcomes.

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Section: Background and Related Workmentioning

confidence: 99%

Multimodal Hierarchical Dirichlet Process-Based Active Perception by a Robot

2018

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“…A robot that has the potential to be an element of an emergent symbol system, i.e., a member of our semiotic society, must be able to explore its environment, acquire knowledge, and communicate with people autonomously. Active perception and active learning are two of the most important capabilities of humans for achieving life-long development and communication [43,133,[136][137][138][139][140][141][142][143][144][145][146][147].…”

Section: Active Perception and Learningmentioning

confidence: 99%

“…Localization, mapping, and navigation are also important targets of active perception [133,141,142]. In addition, various other studies on active perception have been conducted [43,[143][144][145][146][147].…”

Section: Active Perception and Learningmentioning

confidence: 99%

Symbol Emergence in Robotics: A Survey

Taniguchi

Nagai

Nagaoka

et al. 2015

Preprint

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Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially selforganized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-ofart research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory-motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.

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“…In addition, various studies on active perception by a robot have been conducted Gouko et al (2013); Saegusa et al (2011); Ji and Carin (2006); Tuci et al (2010); Natale et al (2004); Schneider et al (2009); Sushkov and Sammut (2012); Hogman et al (2013); Ivaldi et al (2014); Fishel and Loeb (2012); Pape et al (2012). In spite of a large number of contributions about active perception, few theories of active perception for multimodal object category recognition have been proposed.…”

Section: Active Perceptionmentioning

confidence: 99%

Multimodal Hierarchical Dirichlet Process-based Active Perception

Taniguchi,

Takano,

Yoshino

2015

Preprint

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In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object. We propose an MHDP-based active perception method that uses the information gain (IG) maximization criterion and lazy greedy algorithm. We show that the IG maximization criterion is optimal in the sense that the criterion is equivalent to a minimization of the expected Kullback-Leibler divergence between a final recognition state and the recognition state after the next set of actions. However, a straightforward calculation of IG is practically impossible. Therefore, we derive an efficient Monte Carlo approximation method for IG by making use of a property of the MHDP. We also show that the IG has submodular and non-decreasing properties as a set function because of the structure of the graphical model of the MHDP. Therefore, the IG maximization problem is reduced to a submodular maximization problem. This means that greedy and lazy greedy algorithms are effective and have a theoretical justification for their performance. We conducted an experiment using an upper-torso humanoid robot and a second one using synthetic data. The experimental results show that the method enables the robot to select a set of actions that allow it to recognize target objects quickly and accurately. The results support our theoretical outcomes.

show abstract

Online Exploratory Behavior Acquisition of Mobile Robot Based on Reinforcement Learning

Cited by 3 publications

References 6 publications

Multimodal Hierarchical Dirichlet Process-Based Active Perception by a Robot

Multimodal Hierarchical Dirichlet Process-Based Active Perception by a Robot

Symbol Emergence in Robotics: A Survey

Multimodal Hierarchical Dirichlet Process-based Active Perception

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