Cognitive developmental robotics is one of the keys to creating intelligent robots based on human development. In our research, we focus on drawing development of a human infant for creating a developing drawing robot. As the basis of our development model, we adopt five stages development proposed by Luquet. Multiple Timescales Recurrent Neural Network (MTRNN) model, is utilized as the learning model. In the first stage of drawing development, "Scribbling", the robot generates random arm motions to draw meaningless objects to learn the relation between the arm motion and drawing result. The robot arm/pen dynamics is learned using MTRNN in this stage. In the second/third stages, the robot imitates drawing motions from a human to improve its drawing skills to draw shapes through incremental learning. In this paper, we introduce model retraining and motion trajectory modification into the learning model to improve the drawing performance. The method is inspired by the Adaptive VITEWRITE (AVITEWRITE) model proposed by Grossberg, which describes drawing skill improvement of children through perception/action cycle involving vision, attention, learning, and movement. Experiments were conducted using a humanoid robot Nao drawing on a pen tablet. The results of the experiment show the effectiveness of the method improving the drawing performance using the proposed method.

In this paper, we present a method to improve a robot's imitation performance in a drawing scenario by inserting pauses in motion. Human's drawing skills are said to develop through five stages: 1) Scribbling, 2) Fortuitous Realism, 3) Failed Realism, 4) Intellectual Realism, and 5) Visual Realism. We focus on stages 1) and 3) for creating our system, each corresponding to body babbling and imitation learning, respectively. For stage 1), the robot randomly moves its arm to associate robot's arm dynamics with the drawing result. Presuming that the robot has no knowledge about its own dynamics, the robot learns its body dynamics in this stage. For stage 3), we consider a scenario where a robot would imitate a human's drawing motion. Upon creating the system, we focus on the motionese phenomenon, which is one of the key factors for discussing acquisition of a skill through a human parent-child interaction. In motionese, the parent would first show each action elaborately to the child, when teaching a skill. As the child starts to improve, the parent's actions would be simplified. Likewise in our scenario, the human would first insert pauses during the drawing motions where the direction of drawing changes (i.e. corners). As the robot's imitation learning of drawing converges, the human would change to drawing without pauses. The experimental results show that insertion of pause in drawing imitation scenarios greatly improves the robot's drawing performance.

This paper mainly deals with robot developmental learning on drawing and discusses the influences of physical embodiment to the task. Humans are said to develop their drawing skills through five phases: 1) Scribbling, 2) Fortuitous Realism, 3) Failed Realism, 4) Intellectual Realism, 5) Visual Realism. We implement phases 1) and 3) into the humanoid robot NAO, holding a pen, using a neuro dynamical model, namely Multiple Timescales Recurrent Neural Network (MTRNN). For phase 1), we used random arm motion of the robot as body babbling to associate motor dynamics with pen position dynamics. For phase 3), we developed incremental imitation learning to imitate and develop the robot's drawing skill using basic shapes: circle, triangle, and rectangle. We confirmed two notable features from the experiment. First, the drawing was better performed for shapes requiring arm motions used in babbling. Second, performance of clockwise drawing of circle was good from beginning, which is a similar phenomenon that can be observed in human development. The results imply the capability of the model to create a developmental robot relating to human development.

Affordance theory suggests that humans recognize the environment based on invariants. Invariants are features that describe the environment offering behavioral information to humans. Two types of invariants exist, structural invariants and transformational invariants. In our previous paper, we developed a method that self-organizes transformational invariants, or motion features, from camera images based on robot's experiences. The model used a bi-directional technique combining a recurrent neural network for dynamics learning, namely Recurrent Neural Network with Parametric Bias (RNNPB), and a hierarchical neural network for feature extraction. The bi-directional training method developed in the previous work was effective in clustering the motion of objects, but the analysis did not give good segregation results of the self-organized features (transformational invariants) among different motion types. In this paper, we present a refined model which integrates dynamics learning and feature extraction in a single model. The refined model is comprised of Multiple Timescales Recurrent Neural Network (MTRNN), which possesses better learning capability than RNNPB. Self-organization result of four types of motions have proved the model's capability to create clusters of object motions. The analysis showed that the model extracted feature sequences with different characteristics for four object motion types.

We propose neural models for segmenting the area of a body from visual scene based on predictability. Neuroscience has shown that a prediction model in brain, which predicts sensory-feedback from motor command, can divide the sensory-feedback into the self-motion derived feedback and other derived feedback. The prediction model is important for prediction control of the body. Previous studies in robotics of the prediction model assumed that a robot can recognize the position of its body (e.g. its hand) and that the view contains only that body part. In our models, motor commands and visual feedback (pixel image that includes not only a hand but also object and background) are input into a neural network model and then the body area is segmented and prediction model of body is acquired. Our model contains two parts: 1) An object detection model obtains a conversion system between object positions and the pixel image. 2) A movement prediction model predicts hand-object positions from motor commands and identifies the body. We confirmed that our models can segment the body/object area based on their pixel textures and discriminate between them by using prediction error.

Humans are said to unintentionally trace handwriting sequences in their brains based on handwriting experiences when recognizing written text. In this paper, we propose a model for predicting handwriting sequence for written text recognition based on handwriting experiences. The model is first trained using image sequences acquired while writing text. The image features of sequences are self-organized from the images using Self-Organizing Map. The feature sequences are used to train a neuro-dynamics learning model. For recognition, the text image is input into the model for predicting the handwriting sequence and recognition of the text. We conducted two experiments using ten Japanese characters. The results of the experiments show the effectivity of the model.

Predictability is an important factor for determining robot motions. This paper presents a model to generate robot motions based on reliable predictability evaluated through a dynamics learning model which self-organizes object features. The model is composed of a dynamics learning module, namely Recurrent Neural Network with Parametric Bias (RNNPB), and a hierarchical neural network as a feature extraction module. The model inputs raw object images and robot motions. Through bi-directional training of the two models, object features which describe the object motion are self-organized in the output of the hierarchical neural network, which is linked to the input of RNNPB. After training, the model searches for the robot motion with high reliable predictability of object motion. Experiments were performed with the robot's pushing motion with a variety of objects to generate sliding, falling over, bouncing, and rolling motions. For objects with single motion possibility, the robot tended to generate motions that induce the object motion. For objects with two motion possibilities, the robot evenly generated motions that induce the two object motions.

The objective of our study was to develop dynamic collaboration between a human and a robot. Most conventional studies have created pre-designed rule-based collaboration systems to determine the timing and behavior of robots to participate in tasks. Our aim is to introduce the confidence of the task as a criterion for robots to determine their timing and behavior. In this paper, we report the effectiveness of applying reproduction accuracy as a measure for quantitatively evaluating confidence in an object arrangement task. Our method is comprised of three phases. First, we obtain human-robot interaction data through the Wizard of OZ method. Second, the obtained data are trained using a neuro-dynamical system, namely, the Multiple Time-scales Recurrent Neural Network (MTRNN). Finally, the prediction error in MTRNN is applied as a confidence measure to determine the robot's behavior. The robot participated in the task when its confidence was high, while it just observed when its confidence was low. Training data were acquired using an actual robot platform, Hiro. The method was evaluated using a robot simulator. The results revealed that motion trajectories could be precisely reproduced with a high degree of confidence, demonstrating the effectiveness of the method.

Tool-body assimilation is one of the intelligent human abilities. Through trial and experience, humans are capable of using tools as if they are part of their own bodies. This paper presents a method to apply a robot's active sensing experience for creating the tool-body assimilation model. The model is composed of a feature extraction module, dynamics learning module, and a tool recognition module. Self-Organizing Map (SOM) is used for the feature extraction module to extract object features from raw images. Multiple Time-scales Recurrent Neural Network (MTRNN) is used as the dynamics learning module. Parametric Bias (PB) nodes are attached to the weights of MTRNN as second-order network to modulate the behavior of MTRNN based on the tool. The generalization capability of neural networks provide the model the ability to deal with unknown tools. Experiments are performed with HRP-2 using no tool, I-shaped, T-shaped, and L-shaped tools. The distribution of PB values have shown that the model has learned that the robot's dynamic properties change when holding a tool. The results of the experiment show that the tool-body assimilation model is capable of applying to unknown objects to generate goal-oriented motions.

Consistency of object dynamics, which is related to reliable predictability, is an important factor for generating object manipulation motions. This paper proposes a technique to generate autonomous motions based on consistency of object dynamics. The technique resolves two issues: construction of an object dynamics prediction model and evaluation of consistency. The authors utilize Recurrent Neural Network with Parametric Bias to self-organize the dynamics, and link static images to the self-organized dynamics using a hierarchical neural network to deal with the first issue. For evaluation of consistency, the authors have set an evaluation function based on object dynamics relative to robot motor dynamics. Experiments have shown that the method is capable of predicting 90% of unknown object dynamics. Motion generation experiments have proved that the technique is capable of generating autonomous pushing motions that generate consistent rolling motions.

This paper presents a method to autonomously extract object features that describe their dynamics from active sensing experiences. The model is composed of a dynamics learning module and a feature extraction module. Recurrent Neural Network with Parametric Bias (RNNPB) is utilized for the dynamics learning module, learning and self-organizing the sequences of robot and object motions. A hierarchical neural network is linked to the input of RNNPB as the feature extraction module for extracting object features that describe the object motions. The two modules are simultaneously trained using image and motion sequences acquired from the robotpsilas active sensing with objects. Experiments are performed with the robotpsilas pushing motion with a variety of objects to generate sliding, falling over, bouncing, and rolling motions. The results have shown that the model is capable of extracting features that distinguish the characteristics of object dynamics.

Prediction of dynamic features is an important task for determining the manipulation strategies of an object. This paper presents a technique for predicting dynamics of objects relative to the robot's motion from visual images. During the learning phase, the authors use recurrent neural network with parametric bias (RNNPB) to self-organize the dynamics of objects manipulated by the robot into the PB space. The acquired PB values, static images of objects, and robot motor values are input into a hierarchical neural network to link the static images to dynamic features (PB values). The neural network extracts prominent features that induce each object dynamics. For prediction of the motion sequence of an unknown object, the static image of the object and robot motor value are input into the neural network to calculate the PB values. By inputting the PB values into the closed loop RNNPB, the predicted movements of the object relative to the robot motion are calculated sequentially. Experiments were conducted with the humanoid robot Robovie-IIs pushing objects at different heights. Reducted grayscale images and shoulder pitch angles were input into the neural network to predict the dynamics of target objects. The results of the experiment proved that the technique is efficient for predicting the dynamics of the objects.

In this paper, a wide-area recognition technique for mobile robots, which proves to be specifically effective with biped robots, using Hybrid Motion Stereo is presented. The proposed technique uses several cameras and divides the field of view into two groups, namely, areas visible in more than two cameras (MCVA : Multiple Camera Visible Area) and areas visible in only a single camera (SCVA : Single Camera Visible Area). Positional informations contained in MCVA can be computed constantly by stereo vision, while those contained in SCVA require movement of the camera to be computed by motion stereo. Estimation of the movement of cameras can be obtained by calculating the movement of immobile objects in MCVA. The measure of precision in computation by motion stereo depends on the magnitude of the movement of points in the image. Thus, high precision in SCVA can be obtained by lateral or longitudinal motions of the cameras. One such motion can be generated with biped robots for its lateral cyclic motion caused by the movement of the center of gravity for stability retainment. The authors have implemented these methods into the humanoid robot HRP-2 and evaluated the effectivity of the technique.

Robots working in complex environment require accurate perception of a wide field of view. Using cameras, hybrid motion stereo, which combines the computations by stereo vision and motion stereo, is capable of acquiring positional information of the whole field of view. However, the technique generates errors in computation by motion stereo when tracking feature points fails. This paper describes a method to reject the outliers to avoid erroneous recognition. The method uses computation results from several previous images, computing the spatial deviation and temporal deviation of points, and rejecting those considered to be deviated. The evaluation function is composed with a weighted average of the sequential results, each with a weight of the total number of points existing in the neighboring space, which represents the spatial deviation. Experimental results using the humanoid robot HRP-2 denote the effectiveness of the method.