Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> Perceiving, for all its nicety of functioning in the dark room under strict instructions for accuracy, comprises a highly complex series of little understood psychological processes. For under all conditions, perceiving represents a resultant of two complex sets of specifications. One set describes the conditions of stimulation. This is done either in terms of physical measures such as wave length, or in terms of psychological norms such as in the description of a picture as that picture is seen by "normal" observers under optimal conditions and with a set for accuracy. This first set of specifications we are used to calling "stimulus" factors. <s> BIB001 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> Subjects searched sets of items for targets defined by conjunctions of color and form, color and orientation, or color and size. Set size was varied and reaction times (RT) were measured. For many unpracticed subjects, the slopes of the resulting RT X Set Size functions are too shallow to be consistent with Treisman's feature integration model, which proposes serial, self-terminating search for conjunctions. Searches for triple conjunctions (Color X Size X Form) are easier than searches for standard conjunctions and can be independent of set size. A guided search model similar to Hoffman's (1979) two-stage model can account for these data. In the model, parallel processes use information about simple features to guide attention in the search for conjunctions. Triple conjunctions are found more efficiently than standard conjunctions because three parallel processes can guide attention more effectively than two. <s> BIB002 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> We introduce a mobile robot playing at dominoes. The robot is equipped with a pan-tilt unit and a CCD camera and is solely guided by the vision system. Besides the mobile robot the overall system is distributed on a general-purpose workstation. The communication is realized by three radio links transmitting the video signal and two serial data streams for the pan-tilt and the robot controller respectively. Two client/server connections enable a bidirectional data exchange between the image processing software and the robot control and navigation software. The protocol includes requests for certain actions to be performed by the opposite module and the transmission of domino coordinates. The robot identifies the dominoes, collects them, and deposits them in form of a straight chain where two adjacent domino halves possess the same digit. Several iterations of a perception action cycle have to be executed to fulfill this task: The first cycle starts with a visual exploration of the robot's environment and a subsequent explicit foveation of detected domino point clusters. After a coordinate transformation the dominoes are identified by template matching followed by a comparison with a knowledge base to eliminate ambiguities. The positions of the dominoes are registered into the global map space of the robot which position is continuously updated while it is moving. When the robot has reached a so-called virtual point in front of a specified domino the robot requests a control view towards this domino to verify its position. After feedback from the vision system the robot grips the target, moves to the appropriate position in front of the already formed chain, again verifies its position, and deposits the domino. Afterwards the robot returns to its initial position where the cycle starts again with the selection of the next target domino. <s> BIB003 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> Most models of visual search, whether involving overt eye movements or covert shifts of attention, are based on the concept of a saliency map, that is, an explicit two-dimensional map that encodes the saliency or conspicuity of objects in the visual environment. Competition among neurons in this map gives rise to a single winning location that corresponds to the next attended target. Inhibiting this location automatically allows the system to attend to the next most salient location. We describe a detailed computer implementation of such a scheme, focusing on the problem of combining information across modalities, here orientation, intensity and color information, in a purely stimulus-driven manner. The model is applied to common psychophysical stimuli as well as to a very demanding visual search task. Its successful performance is used to address the extent to which the primate visual system carries out visual search via one or more such saliency maps and how this can be tested. <s> BIB004 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> An active approach to sensing can provide the focused measurement capability over a wide field of view which allows correctly formulated simultaneous localization and map-building (SLAM) to be implemented with vision, permitting repeatable longterm localization using only naturally occurring, automatically-detected features. In this paper, we present the first example of a general system for autonomous localization using active vision, enabled here by a high-performance stereo head, addressing such issues as uncertainty-based measurement selection, automatic map-maintenance, and goal-directed steering. We present varied real-time experiments in a complex environment. <s> BIB005 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> This paper argues how robot learning can be accelerated by a developmental approach that changes the capability of the learning robot (robot development) and/or the complexity of the environment (environmental development). "Robot development" means that the perceptual, cognitive, and behavioral capabilities of the learning robot change from immature to mature states in accordance with learning process. On the other hand, "environmental development" means that the complexity of the environmental structure changes from simple to complicated ones in accordance with learning process. Then, the developmental approaches can be categorized according to the relationships between what does develop and what does make the development. A case study of developmental robot learning for joint attention in which a caregiver can be regarded as a part of the environment is given to show how it can accelerate the learning. Finally, future work and discussion are given. <s> BIB006 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> In order to enable the widespread use of robots in home and office environments, systems with natural interaction capabilities have to be developed. A prerequisite for natural interaction is the robot's ability to automatically recognize when and how long a person's attention is directed towards it for communication. As in open environments several persons can be present simultaneously, the detection of the communication partner is of particular importance. In this paper we present an attention system for a mobile robot which enables the robot to shift its attention to the person of interest and to maintain attention during interaction. Our approach is based on a method for multi-modal person tracking which uses a pan-tilt camera for face recognition, two microphones for sound source localization, and a laser range finder for leg detection. Shifting of attention is realized by turning the camera into the direction of the person which is currently speaking. From the orientation of the head it is decided whether the speaker addresses the robot. The performance of the proposed approach is demonstrated with an evaluation. In addition, qualitative results from the performance of the robot at the exhibition part of the ICVS'03 are provided. <s> BIB007 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> Social robots are receiving much interest in the robotics community. The most important goal for such robots lies in their interaction capabilities. An attention system is crucial, both as a filter to center the robot’s perceptual resources and as a mean of letting the observer know that the robot has intentionality. In this paper a simple but flexible and functional attentional model is described. The model, which has been implemented in an interactive robot currently under development, fuses both visual and auditive information extracted from the robot’s environment, and can incorporate knowledge-based influences on attention. <s> BIB008 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> A human-like intelligent robot is expected to have the capability to develop its cognitive functions through experience without a priori knowledge or explicit teaching. In addition, the realization of this kind of robot leads us to understand the developmental mechanisms of human beings. This paper proposes a bootstrap learning model by which a robot acquires the ability of joint attention without a caregiver's evaluation or a controlled environment based on the robot's embedded mechanisms: visual attention and learning with self-evaluation. Through learning based on the proposed model, the robot finds a correlation in sensorimotor coordination when joint attention succeeds and consequently acquires the ability of joint attention by accumulating the appropriate correlation and losing the uncorrelated coordination as statistical outliers. The experimental results show the validity of the proposed model. <s> BIB009 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> This article discusses the concept of joint attention and the different skills underlying its development. Research in developmental psychology clearly states that the development of skills to understand, manipulate and coordinate attentional behavior plays a pivotal role for imitation, social cognition and the development of language. However, beside the fact that joint attention has recently received an increasing interest in the robotics community, existing models concentrate only on partial and isolated elements of these phenomena. In the line of Tomasello’s research, we argue that joint attention is much more than simultaneous looking because it implies a shared intentional relation to the world. This requires skills for attention detection, attention manipulation, social coordination and, most importantly, intentional understanding. After defining joint attention and its challenges, the current state-of-the-art of robotic and computational models relevant for this issue is discussed in relation to a developmental timeline drawn from results in child studies. From this survey, we identify open issues and challenges that still need to be addressed to understand the development of the various aspects of joint attention and conclude with the potential contribution of robotic models. <s> BIB010 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> This study presents experiments on the imitative interactions between a small humanoid robot and a user. A dynamic neural network model of a mirror system was implemented in a humanoid robot, based on the recurrent neural network model with parametric bias (RNNPB). The experiments showed that after the robot learns multiple cyclic movement patterns as embedded in the RNNPB, it can regenerate each pattern synchronously with the movements of a human who is demonstrating the corresponding movement pattern in front of the robot. Further, the robot exhibits diverse interactive responses when the user demonstrates novel cyclic movement patterns. Those responses were analyzed and categorized. We propose that the dynamics of coherence and incoherence between the robot’s and the user’s movements could enhance close interactions between them, and that they could also explain the essential psychological mechanism of joint attention. <s> BIB011 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> This paper studies a synthetic model for joint attentions andturn takingbyconductingexperimentsofrobot-userimitative interactions. A Sony humanoid robot learns multiple cyclic movement patterns as embedded in a neural network model proposed by us and each of memorized movement patterns can be retrieved and generated by means of entrainment by external inputs in terms of users’ movement patterns perceived. Our experiments on a simple imitation game showed that multiple movement patterns are generated as synchronized between the robot and users while the shared patterns shift spontaneously from one to another. Based on the experimental results, we show possible dynamical systems accounts for the underlying mechanisms for joint attentions and turn taking. <s> BIB012 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> Robot companions are intended for operation in private homes with naive users. For this purpose, they need to be endowed with natural interaction capabilities. Additionally, such robots will need to be taught unknown objects that are present in private homes. We present a multi-modal object attention system that is able to identify objects referenced by the user with gestures and verbal instructions. The proposed system can detect known and unknown objects and stores newly acquired object information in a scene model for later retrieval. This way, the growing knowledge base of the robot companion improves the interaction quality as the robot can more easily focus its attention on objects it has been taught previously. <s> BIB013 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> A major challenge for the realization of intelligent robots is to supply them with cognitive abilities in order to allow ordinary users to program them easily and intuitively. One approach to such programming is teaching work tasks by interactive demonstration. To make this effective and convenient for the user, the machine must be capable of establishing a common focus of attention and be able to use and integrate spoken instructions, visual perception, and non-verbal clues like gestural commands. We report progress in building a hybrid architecture that combines statistical methods, neural networks, and finite state machines into an integrated system for instructing grasping tasks by man-machine interaction. The system combines the GRAVIS-robot for visual attention and gestural instruction with an intelligent interface for speech recognition and linguistic interpretation, and a modality fusion module to allow multi-modal task-oriented man-machine communication with respect to dextrous robot manipulation of objects. <s> BIB014 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> This paper presents a framework for a visual search behavior of a 3D object in a 3D environment performed by HRP-2 humanoid robot. Object search is a sensor planning problem in which appropriate sensor configuration must be selected in order to allow a proper recognition. The sensor planning is formulated as an optimization problem in which the goal is to maximize the target detection probability while minimizing the energy/distance and time to achieve the task. The knowledge of the target position is encoded in a probability distribution map which will be updated after each sensing operation. Specificities of the humanoid robot as well as the characteristics of the recognition system are taken into account to restrict the sensor configuration space to meet heavy time constraints on the behavior in order to have a reactive and near human like reaction time. <s> BIB015 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> Abstract Imitation has been receiving increasing attention from the viewpoint of not simply generating new motions but also the emergence of communication. This paper proposes a system for a humanoid who obtains new motions through learning the interaction rules with a human partner based on the assumption of the mirror system. First, a humanoid learns the correspondence between its own posture and the partner’s one on the ISOMAPs supposing that a human partner imitates the robot motions. Based on this correspondence, the robot can easily transfer the observed partner’s gestures to its own motion. Then, this correspondence enables a robot to acquire the new motion primitive for the interaction. Furthermore, through this process, the humanoid learns an interaction rule that control gesture turn-taking. The preliminary results and future issues are given. <s> BIB016 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> Joint attention is the skill of attending to the same object another person is looking at. The acquisition of this skill is crucial in children for the development of many social and communicative abilities, and has been proposed as a critical social capability for interactive robots. Although recent attempts to model the acquisition of this skill on a robot have been moderately successful (Nagai et al., 2003; Triesch et al., 2006), they all assume that the robot remains passive during the learning process. Infants, on the other hand, have already acquired some rudimentary sensorimotor skills by the time they start to learn joint attention. We believe that these sensorimotor skills can jumpstart and considerably accelerate the learning of joint attention. In this paper we demonstrate on a humanoid robot how to use pointing and reaching to accelerate the learning of joint attention. We show that a robot can acquire this skill with a 95 % accuracy after a total of only 220 training samples compared to 85% accuracy after totals of 10,000+ samples in other approaches. <s> BIB017 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> Imitation learning, or programming by demonstration (PbD), holds the promise of allowing robots to acquire skills from humans with domain-specific knowledge, who nonetheless are inexperienced at programming robots. We have prototyped a real-time, closed-loop system for teaching a humanoid robot to interact with objects in its environment. The system uses nonparametric Bayesian inference to determine an optimal action given a configuration of objects in the world and a desired future configuration. We describe our prototype implementation, show imitation of simple motor acts on a humanoid robot, and discuss extensions to the system <s> BIB018 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> In this paper, we present a strategy whereby a robot acquires the capability to learn by imitation following a developmental pathway consisting on three levels: 1) sensory-motor coordination; 2) world interaction; and 3) imitation. With these stages, the system is able to learn tasks by imitating human demonstrators. We describe results of the different developmental stages, involving perceptual and motor skills, implemented in our humanoid robot, Baltazar. At each stage, the system's attention is drawn toward different entities: its own body and, later on, objects and people. Our main contributions are the general architecture and the implementation of all the necessary modules until imitation capabilities are eventually acquired by the robot. Also, several other contributions are made at each level: learning of sensory-motor maps for redundant robots, a novel method for learning how to grasp objects, and a framework for learning task description from observation for program-level imitation. Finally, vision is used extensively as the sole sensing modality (sometimes in a simplified setting) avoiding the need for special data-acquisition hardware <s> BIB019 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> This paper is centered around landmark detection, tracking, and matching for visual simultaneous localization and mapping using a monocular vision system with active gaze control. We present a system that specializes in creating and maintaining a sparse set of landmarks based on a biologically motivated feature-selection strategy. A visual attention system detects salient features that are highly discriminative and ideal candidates for visual landmarks that are easy to redetect. Features are tracked over several frames to determine stable landmarks and to estimate their 3-D position in the environment. Matching of current landmarks to database entries enables loop closing. Active gaze control allows us to overcome some of the limitations of using a monocular vision system with a relatively small field of view. It supports 1) the tracking of landmarks that enable a better pose estimation, 2) the exploration of regions without landmarks to obtain a better distribution of landmarks in the environment, and 3) the active redetection of landmarks to enable loop closing in situations in which a fixed camera fails to close the loop. Several real-world experiments show that accurate pose estimation is obtained with the presented system and that active camera control outperforms the passive approach. <s> BIB020 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> Enactivism claims that sensory-motor activity and embodiment are crucial in perceiving the environment and that machine vision could be a much simpler business if considered in this context. However, computational models of enactive vision are very rare and often rely on handcrafted control systems. In this article, we argue that the apparent complexity of the environment and of the robot brain can be significantly simplified if perception, behavior, and learning are allowed to co-develop on the same timescale. In doing so, robots become sensitive to, and actively exploit, characteristics of the environment that they can tackle within their own computational and physical constraints. We describe the application of this methodology in three sets of experiments: shape discrimination, car driving, and wheeled robot navigation. A further set of experiments, where the visual system can develop the receptive fields by means of unsupervised Hebbian learning, demonstrates that the receptive fields are consistently and significantly affected by the behavior of the system and differ from those predicted by most computational models of the visual cortex. Finally, we show that our robots can also replicate the performance deficiencies observed in experiments of motor deprivation with kittens. <s> BIB021 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> Simultaneous learning of multiple functions is one of the fundamental issues not only to design intelligent robots but also to understand humanpsilas cognitive developmental process since we, human, do so in our daily lives but we do not know how to do. Drawing an analogy to the well-known bias in child language development, we propose the mutual exclusivity selection principle (muX principle) for learning multi-modal mappings: selecting more mutually exclusive output leads experiences to make underdeveloped complementary mappings more disambiguated. The muX principle is applied to multi-modal joint attention with utterances for lexicon acquisition, and synthetically modeled in both intra- and inter-module levels of output. Through the series of computer simulations, the effects of the muX principle on the mutual facilitation in learning multi-functions and robustness against errors in segmentation of observation are analyzed. Finally, the correspondence of the synthesized development to infantpsilas one is argued based on the simulation with careful behavior by a caregiver. <s> BIB022 </s> Visual Attention for Robotic Cognition: A Survey <s> B. Application-Specific Visual Attention Models <s> In order to achieve visual-guided object manipulation tasks via learning by example, the current neuro-robotics study considers integration of two essential mechanisms of visual attention and arm/hand movement and their adaptive coordination. The present study proposes a new dynamic neural network model in which visual attention and motor behavior are associated with task specific manners by learning with self-organizing functional hierarchy required for the cognitive tasks. The top-down visual attention provides a goal-directed shift sequence in a visual scan path and it can guide a generation of a motor plan for hand movement during action by reinforcement and inhibition learning. The proposed model can automatically generate the corresponding goal-directed actions with regards to the current sensory states including visual stimuli and body postures. The experiments show that developmental learning from basic actions to combinational ones can achieve certain generalizations in learning by which some novel behaviors without prior learning can be successfully generated. <s> BIB023
The application-specific visual attention models are tuned to the applications they are developed for. Visual attention mechanism has at least two properties which can be tuned in an application specific manner. • Selectivity: The basic idea of attention is to focus on a relevant visual stimulus for further processing. The "relevancy" of a stimulus can be defined in terms of its similarity with a set of predefined task-relevant features. The irrelevant information in the visual scene are not considered for further processing and thereby reducing the computational load of an artificial system. • Visual Search: Visual search is an important property of the primates visual attention mechanism which helps to focus on the target-related information in relative exclusion of the others. Thus, the visual search is an special case of demonstration of selectivity. The success of a visual search and the time requirement depends on the number of distractor stimuli present in the visual field and the number of features they share with the target stimulus. Exploitation of these two properties often causes visual attention to reduce to a tracking problem in many application-specific models of visual attention. In case of attention-based tracking, many of the research issues stated in Section III-B do not arise. For instance, the object that is to be tracked is learned once and is tracked in the subsequent camera frames. Each incoming camera frame is searched for this specific object. Thus, there is no need to implement the IOR and the change of coordinates does not have any significance effect on the tracking decision [hence, no need to address (Issues 1.1, 1.2) ]. An example of such attention-based tracking is demonstrated in BIB020 . Here, a covert model of visual attention VOCUS  is used to perform simultaneous localization and mapping (SLAM) by a mobile robot BIB020 . The role of the attention model is to identify the most salient stimuli in the scene and then keep on tracking that specific stimuli in the successive frames by adjusting the camera head. Similar strategy of attention-based tracking is also adopted in BIB005 for vision-based SLAM by mobile robots. To deal with the partial appearance of object (Issue 1.3 ) the model in BIB020 adopts the strategy that the landmarks that reside at the center of the visual field are given higher priority as it is likely that they can be tracked for an extended period of time. The attention model in BIB015 exploits the principle of visual search for robot navigation and mapping. The robot learns the visual features of a set of objects during an off-line training phase. During the autonomous navigation the robot searches for the learned objects, which appear as landmarks, in natural indoor environment. A number of important parameters of the navigation model is chosen based on the off-line training phase. The objects location are projected in an ego-centric frame of reference in order to update a 3-D occupancy grid which contains the information about the landmarks/obstacles in the robot's workspace (Issues 1.1, 1.2, 1. 3). The model described in  is dedicated to design a Bayesian approach of fast visual search for human faces in a video stream. To achieve faster response the attention model sacrifices all other visual information except the intensity feature. Similar to BIB020 , this model  also considers each incoming frame as an isolated static image and does not implement IOR. Similar kind of attention model (focusing on the visual search) is also proposed in BIB007 . Here, the robot is provided with a predefined set of features to search for, e.g., a talking person, human face, human legs located at the closest distance, etc. The robot then uses its multimodal perception to search for this features and attend to them. The task-specific attention model proposed in BIB003 performs visual search for prespecified object patterns (dominos) and executes manipulative actions based on their 3-D locations. The attention model in BIB008 is designed for social interaction with human. The model uses omni-directional camera and the nature of images obtained from such cameras enables the visual features to be registered directly in an ego-centric frame of reference. This inherently offers a solution to the problem of coordinate change (Issue 1.1), dynamic IOR (Issue 1.2) , and partial appearance (Issue 1.3) . The neural-network-based attention models in BIB021 and BIB023 performs navigation and object manipulation while combining the top-down and bottom up influence. The learning mechanism employed in these models BIB021 , BIB023 as a part of the enactive vision system enable them to address the issues of prior training and coordinate change. The visual attention models developed for HRI mostly consider attention as a step toward making the robots cognitive. Visual attention plays a significant role in HRI in order to establish joint attention BIB010 between the robot and the human. Establishing joint attention between a human and robot requires that a robot should be able to detect and manipulate the attention of the human, socially interact with the human, and finally see itself as well as the human as intentional agents. Joint attention, therefore, is an excellent tool to build a meaningful HRI system. A basic requirement of joint attention is that the robot should posses a human like attention model with the capacity to manipulate attention of other agent, as well as of being manipulated by other agents. The visual attention models proposed in HRI literature, therefore, have strong emphasis on top-down modulation of attention. A number of approaches, inspired by the cognitive development of human child, are available to model the top-down influence in attention selection, e.g., imitation learning, scaffolding. These methodologies have their own unique way of addressing the issues sated in Section III-B. In some cases, however, their way of addressing one issue severs the consequence of the others. In case of imitation-based learning of attention, the robot imitates the movements (head/eye/hand) of a person (the user or the operator) to exhibit overt attention behavior BIB016 . Thus the top-down bias appears as the commands from the human operator conveyed through natural speech, hand gesture, gaze direction, etc. For instance, the models in BIB009 and BIB006 evaluate the gaze direction of the user to identify the object of interest to attend. Thus, the model guides a robot to look at the objects to which its user is also looking and thereby establishing simultaneous looking behavior which is a major requirement of joint attention BIB010 . The work in BIB017 uses the head pose and eye-gaze direction of the user to identify the object to attend. To further enhance the quality of joint attention it uses pointing behavior by the robot once it attends to an object. The shared attention model in BIB018 and  uses the gaze direction as a cue to decide which object to attend. An integration of imitation learning and visual search is observed in the connectionist model of joint attention reported in BIB011 and BIB012 where the robot learns a set of motion patterns in an off-line training phase and reproduces them when it finds similar kind of motion pattern performed by the user. The model introduced in BIB022 performs overt attention based on gaze direction of the user as well as spoken command. A major complexity of imitation learning is in order to be accurate it requires the robot to have an efficient learning strategy to conceptualize the underlying goal of the imitative actions and form knowledge from that BIB019 . In other words, the robot has to decide on its own about "what to imitate?" which by itself, is a type of skill that requires cognition. A bit more relaxed approach (with respect to the amount of cognitive load on the robot) as compared to imitation learning is attention mediated by scaffolding BIB001 . Here the idea is to explicitly attract the attention of the robot to certain specific stimuli through different kind of actions, e.g., verbal command, hand-gesture, and motionese. For instance, the attention model in BIB013 uses hand-gesture and verbal command to guide the attention of the robot toward novel objects. Similar approach of attention guiding has been used in BIB014 in order to perform grasping task by a robot manipulator. The attention model in  uses motionese in order to make certain stimuli to appear as extremely salient in the robot's perception. The model  , however, relies on NVT BIB004 for calculation of saliency, does not implement any form of IOR, and operates in a pure off-line fashion. The attention model developed for HRI in  is based on the psychophysical model of visual search proposed in BIB002 . The model is sensitive to task-specific stimuli (e.g., human face, toys with specific color) and attends to them based on the task-context. This model also uses motionese to guide the robot's attention toward certain specific stimuli. The model performs the IOR and the habituation effect with moving camera but does not mention explicitly how the issues involved with camera movement have been addressed. The imitation learning approach and scaffolding relieve a visual attention model from worrying about the issues such as change of image coordinates (Issue 1.1) , implementation of IOR (Issue 1.2) , partial appearance of the objects (Issue 1.3) , and generality (Issue 3). The human operator takes care of these issues and the robot's attention model just mimics the operator. Such a huge benefit, however, comes with the heavy price that a human operator must be dedicated for a robot, which is often an unrealistic demand for autonomous robotic applications. For quick reference, the Table II shows a comparative analysis of the application-specific attention models discussed in this section.