智能机器人材料3

2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) October 31-November 2, 2013 / Ramada Plaza Jeju Hotel, Jeju, KoreaEmotional Gait Generation Method based on Emotion Mental Model - Preliminary experiment with Happiness and Sadness Matthieu Destephe1, Kenji Hashimoto2 and Atsuo Takanishi3Graduate School of Science and Engineering, Waseda University, Tokyo, Japan 2 Faculty of Science and Engineering, Waseda University, Tokyo, Japan 3 Department of Modern Mechanical Engineering & Humanoid Robotics Institute, Waseda University, Tokyo, Japan (Tel : +81-3-3203-4394; E-mail: contact@takanishi.mech.waseda.ac.jp)1Abstract – Designing humanoid robots able to interact socially with humans is a challenging task. If we want the robots to be actively integrated in our society, several issues have to be taken in account: the look of the robot, the naturalness of its movements, the stability of its walk, the reactivity it might have with its human partners. We propose to improve the reactivity of the robot by using a emotional mental model in order to generate emotional gait patterns. Those patterns will help the understanding of any emotional message conveyed between a human and a robot. We propose a novel emotional gait generation method based on the Emotion mental model. We did preliminary experiments with the Happiness and Sadness emotions and with different intensities. Keywords - Motion Generation, Emotion, Biped Robot, Social Robotics1. IntroductionHumanoid robots are designed to interact with people in their daily life and at any age, as soon as kindergarten or as late as nursing home. Advanced robots such as robot companions, robot workers, etc., will need to be able to adapt their behavior according to human feedback. For humans it is important to be able to give and to be given such feedback in a natural way, e.g., using emotional expression. Expressive robots can act as caregivers for children, with or without disabilities, and help their emotional development and wellbeing through emotive interaction. Therefore the ability of expressing emotions is important to facilitate intuitive human robot interaction. Moreover, the adaptation of the robot movements to the interaction context is necessary in order to create an interaction as natural and beneficial as possible. In this context, several emotion capable robots were developed along the years. For example, the robot Kismet was designed to simulate emotion and assess the affective intent of the caregiver [1]; NAO a small humanoid (58 cm) is often used in Human Robot Interaction (HRI) studies with children [2]; and the Waseda KOBIAN (fig. 1), designed by the applicant's team, combines a face capable of human-like expressions (24 DoF) and the bipedal locomotion ability. Preliminary studies on KOBIAN showed that whole-body posture clearly improves the emotion recognition [3].However, if we want to perform a smooth and natural Human Robot Interaction, it necessitates a dynamic interaction between the participants with feedback, which could be visual, audible or tactile. Most of the current robots are only focused on the facial expressions and use rarely their limbs [4-5]. It was showed that the use of whole-body to express emotions improves the recognition rate of the emotions, and thus could increase the understanding and feedback during an interaction. In the case where movements are used for the interaction, they are usually fixed and follow a pre-determined pattern. This means that the robot will follow the same stimuli-response pattern. However, emotions are known to be dependent on several factors such as interaction context, culture, age, and gender. Without dynamic adaptation, after some time, the human partner will become progressively bored and the human implication in the interaction will drop. Additionally, the emotive walking research is an innovative field of research which stays mainly unexplored. In this paper, we propose an emotional gait generation method based on the Emotion mental model [6]. After a brief literature review in section 2, we describe our robot platform, the emotional mental model and a new emotional gait generation method in section 3. We present an experiment in the section 4 and we conclude and comment our work in the section 5.2. Related works2.1 Humanoid robots Among human sized humanoids robots, just a few are capable of expressing emotions. HRP-4C is a geminoid which can express pre-programmed facial emotions but cannot walk [7]. ASIMO [8] and WABIAN-2RII [9] are able to walk but do not have emotion expression capabilities. KIBO [10], developed by KIST, can express facial emotion expression but this capability was not assess by research. KOBIAN-2R [11], developed at Waseda University, is able to walk and express emotions not only with its face but also with its whole body [13]. 2.2 Emotion models Emotion models can be classified in three categories: appraisal, a categorical or a dimensional approach. The appraisal approach states that our appreciation of events (appraisal) determines the reaction to those events and it is978-1-4799-1197-4/13/$31.00 ©2013 IEEE86unique to each individual. The categorical approach uses discrete labeling in order to define emotion. The dimensional approach, instead of defining emotion with a label or with a reaction to an event, uses different dimensions (for ex: pleasure, dominance, arousal, certainty, etc.) to define an emotion. The values of all the parameters will determine the emotion. In the robotics community, the dimensional approach with the well known Pleasure-Arousal-Dominance (PAD) model [13-14].eyelids, 7-DoFs for lip, and 1-DoF for jaw) which can perform hundreds of facial expressions. KOBIAN-R has 7-DoFs anthropomorphic arms consisting of a shoulder part (pitch, yaw and roll axis), an elbow part (pitch axis), and a wrist part (pitch, yaw and roll axis). The system configuration of KOBIAN-R is presented in the Fig. 3. KOBIAN-R's control system is a hybrid of a centralized control system in the body with a main PC (CPU: Pentium M 1.6GHz, RAM: 2GB, OS: QNX Neutrino 6.3.0) and a distributed control system in the head with the main PC and 7 motor controller units.3. Methods3.1 Humanoid platform: KOBIAN-RFig. 3. KOBIAN-R System Configuration 3.2 Emotional mental space A. Basic description Fig. 1. KOBIAN-R Miwa et.al developed a mental model for humanoid robot [6]. The flow of information of external stimuli and robot's inner state is presented in Fig. 4. Using that model, the emotions "felt" by a robot can change dynamically according to external stimuli captured by its sensors (i.e: heavy sound, slap, odor of alcohol) and the robot's personality. The Mental Dynamics, which are the mental changes caused by internal and external stimuli, is extremely important for the emotional expression. The structure of the mental model follows a simplified human brain model which has three layers: reflex, emotion and intelligence (Fig. 4).ZŽďŽƚ WĞƌƐŽŶĂůŝƚLJ‫ٻ‬ Fig. 2. KOBIAN-R DOF configurationIn 2010, we have developed Emotion Expression Biped Humanoid Robot KOBIAN-R (Fig. 1) which is capable of both performing bipedal walking and facial expression. It was created as a combination of the humanoid robots WE-4RII [15] and WABIAN-2R [9], integrating walking capability of WABIAN-2R with the emotion capable upper body of WE-4RII. This robot has 65-DoFs (Fig. 2) and its total weight is 62 kg. It has two CCD camera, 2 six-axis sensors in its feet. An embedded computer controls the motion and it can be operated without external source of power thanks to its batteries. The head is equipped with 24-DoFs (8-DoFs for eyebrows, 5-DoFs for^ĞŶƐŝŶŐ WĞƌƐŽŶĂůŝƚLJ ZĞĐŽŐŶŝƚŝŽŶ ^ĞŶƐŝŶŐŵŽƚŝŽŶ /ŶƚĞůůŝŐĞŶĐĞ ZĞĨůĞdž ZŽďŽƚ džƚĞƌŶĂů ŶǀŝƌŽŶŵĞŶƚdžƉƌĞƐƐŝŽŶ WĞƌƐŽŶĂůŝƚLJ ĞŚĂǀŝŽƌ DŽƚŝŽŶFig. 4. Basic information processing structure87The emotion mapping is represented by 3 dimensions (Activation, Pleasantness and Certainty) (Fig. 5). The Activation axis represents the degree of activity of the robot and indicates how often the external stimuli information are updated. The Pleasantness qualifies subjectively the overall experience as positive (> 0) or negative (< 0). The Certainty axis describes how much the robot trusts the information given by its sensors. Seven basic emotions are mapped in this space (Fig. 5).ĞƌƚĂŝŶƚLJ ĐƚŝǀĂƚŝŽŶ ŶŐĞƌ ŝƐŐƵƐƚ ^ƵƌƉƌŝƐĞ3.3 Emotional gait generation method A. Motion Capture We asked two professional, Japanese actors (each 22 years old, one male and one female) to perform several types of emotive walking (sadness, happiness, anger and fear) with different intensities (low, middle, high, and exaggerated) [16]. The actors have been instructed a few days ahead of the experiment to prepare a scenario and to perform the first three intensities in such a way that they would correspond to natural occurrences of emotion expression. The exaggerated intensity on the other hand should be performed with extravagant theatricality, broad gestures and overplayed expressions, comparable to emotions expressions seen in plays and theaters. From the motion recordings, we extracted the step height (height from the ankle to the ground), step length (length from right heel to left heel), velocity, head pitch, shoulder pitch and waist pitch. We chose those values as parameters for the representation of the emotional walking patterns. The motion capture values are normalized in order to be usable by the robot. B. Emotional Gait Parameters Our motion pattern generator can take as input (usually by hand) among others the following seven parameters: step length, step height, phase duration, head pitch, shoulder pitch, elbow pitch, waist pitch. From the data captured [16], we extract values for the previous parameters. We use for each parameter a quadratic function in order to model their evolution as the emotional intensity increases (Eq.1 and Eq. 2). Given x, an intensity value along the Activation axis in the range of [-1, 1], H and S, two 3 x 7 vectors (3 parameters for each quadratic equation, 7 parameters),ĂƉƉŝŶĞƐƐ WůĞĂƐĂŶƚŶĞƐƐ ^ůĞĞƉŵŽƚŝŽŶ sĞĐƚŽƌ E &ĞĂƌ^ĂĚŶĞƐƐ ^ůĞĞƉFig. 5. Emotional Mental space The first robot which used this model was WE-4RII, which was able to change its mental state according to the external (vision, smell, hearing, tactile sensors) and internal stimuli (personality), and expresses its emotion using facial expressions, facial color and body movement. The second is KOBIAN-R. B. Emotion Vector and Equations of Emotion The Emotion Vector E is described the Equations of Emotion if the robot senses any stimuli with its sensors. The internal mental dynamics is similar to the evolution a human mind might have. This dynamics is expressed by equations similar to the equation of motion. The equations of emotion were expended into the second order differential equation which modeled the equation of motion (Fig. 6).H a x2 + Hb x + H c = 0i i i(1) (2)S a x 2 + Sb x + S c = 0i i iAlong the Activation axis, low intensity values range between -0.25 and -0.75, middle values between -0.75 and 0.25, high values between 0.25 and 0.75.4. ExperimentIn order to verify our emotional gait generation, we focused on two emotions: Happiness and Sadness. We also chose 3 intensities: low, middle, high. We performed two simulations with Matlab, one for each emotion and the patterns generated are shown with our pattern simulator. We made the emotion state of the robot change every 6 steps. We constrained the Certainty and Pleasantness in order to obtain the desired emotions. Figure 7 and Figure 8 represent respectively the Happy and the Sadness we obtained. From left to right, different intensities are represented: low, middle and high. Fig. 6. Emotion Vector88Fig. 7. Happy (low, middle, high)Fig. 8. Sadness (low, middle, high)5. ConclusionIn this paper we proposed a novel emotional gait generation method based on a emotion mental model we had developed. The gait is subject to change according to emotional intensity. We simulated several emotional gaits with different intensities. The work was done in simulation and we plan to use the generated patterns with the real KOBIAN. We also plan to use the other emotions and create a complete framework.AcknowledgementThis work was supported in part by Global COE Program "Global Robot Academia", MEXT, Japan. It is also partially supported by SolidWorks Japan K.K. and DYDEN Corporation The High-Performance Physical Modeling and Simulation software MapleSim used in our research was provided by Cybernet Sys-tems Co.,Ltd. (Vendor: Waterloo Maple Inc.). This study was conducted as part of the Research Institute for Science and Engineering, andas part of the humanoid project at the Humanoid Robotics Institute, both at Waseda University.References[1] C. Breazal, Designing sociable robots: MIT press, (2002). [2] P. Baxter, T. Belpaeme, and L. Cañamero, “Long-term human-robot interaction with young users,”IEEE/ACM Human-Robot Interaction 2011 Conference, (2011) [3] M. Zecca, Y. Mizoguchi, K. Endo, F. Iida, Y. Kawabata, N. 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