International Journal of Humanoid Robotics

Animated agents that act and speak as attendants to guests on shopping web sites are becoming increasingly popular. Inspired by this development, we propose a new method of presentation using a humanoid robot. Humanoid presentations are effective in a real environment because they can move and look around at the audience similar to a human presenter. We developed a simple script language for multimodal presentations by a humanoid robot called MPML-HR, which is a descendant of the Multimodal Presentation Markup Language (MPML) originally developed for animated agents. MPML-HR allows many non-specialists to easily write multimodal presentations for a humanoid robot. We further evaluated humanoid robots' presentation ability using MPML-HR to find the difference in audience impressions between the humanoid robot and the animated agent. Psychological evaluation was conducted to compare the impressions of a humanoid robot's presentation with an animated agent's presentation. Using the Semantic Differential (SD) method and direct questioning, we measured the difference in audience impressions between an animated agent and a humanoid robot.

Recently, there has been a growing interest in human–robot interaction. Researchers in artificial intelligence and robotics have built various types of social robots which can express emotions through speech, facial expressions and hand gestures. Although some of these robots are able to interact with humans in interesting ways, they cannot move as naturally as we do because of the limited number of degrees of freedom in their body torsos (some of them do not even have a torso). Since we often express and perceive each other's emotions and motives at a distance using body language alone, it would be good for the next generation of humanoid robots to possess similar capabilities. As a first step towards this goal, we developed a 28-DOF full-body humanoid robot as an experimental platform. Unlike the current generation of humanoid robots, our robot has a flexible spine. This feature is very important because counterbalancing movements of the spine are required to maintain dynamic stability in humans and humanoid robots. Our robot can belly dance and communicate affective motions via full-body movements. Using a Central Pattern Generator (CPG) based controller, we generated rhythmic motions for the arms, upper and lower bodies. We then conducted psychological experiments using both the robot and a human actors. Statistical analyses were carried out to test our hypotheses on human perception of affective movements. Experimental results show that human subjects were able to perceive emotions from the robot based only on its body motions, sometimes as well as recognizing the movements being performed by the human actor. Our robot can be used to examine the relationship between the movement of the spine, shoulders, arms, neck and head when attempting to reproduce affective movements. Psychologists, actors, dancers and animators can benefit from this line of research by learning how emotions can be conveyed through body motions and knowing how body part movements combine to communicate emotional expressions.

This work is concerned with the integrated dynamic control of humanoid locomotion mechanisms based on the spatial dynamic model of the humanoid mechanism, a servo system model, and an environment model. The control scheme was synthesized using the centralized model of the system and the hierarchical principle, with tactical and executive control levels. The proposed structure of the dynamic controller involves four feedback loops: position-velocity feedback at the robotic mechanism joints, dynamic reaction feedback at Zero-Moment Point, impact force feedback at the instant when the foot strikes the ground, and the load feedback of the mechanism joints. Simulation experiments are carried out for a number of characteristic examples. The numerical results obtained, along with theoretical study, serve as the basis for a critical evaluation of the performance of the devised controller.

A humanoid is a robot that looks like a human (it has human shape, with a trunk, two arms, two legs and a head) and has been specially designed to act like a human being. This paper presents the procedures and results of a series of tests consisting of the reproduction of some target humanoid movements (walking, sitting, standing up, etc.) by a 4-DOF spherical motion base. In this way, an operator seated on the motion base will be able to feel the movements of the humanoid, creating a feeling of teleexistence. This paper contributes to the improvement and development of the teleexistence/telepresence technology applied to humanoid research.

Unlike wheeled robots, humanoid robots are able to cross obstacles by stepping over or upon them. Conventional 2D methods for robot navigation fail to exploit this unique ability of humanoids and thus design trajectories only by circumventing obstacles. Recently, global algorithms have been presented that take into account this feature of humanoids. However, due to high computational complexity, most of them are very time consuming. In this paper, we present a new approach to footstep planning in obstacle cluttered environments that employs a human-like strategy to terrain traversal. Simulation results of its implementation on a model of the Saika-3 humanoid robot are also presented. The algorithm, being one of reactive nature, refutes previous claims that reactive algorithms fail to find successful paths in complex obstacle cluttered environments.

In human heads there is a strong structural linkage between the vocal tract and facial behavior during speech. For a robotic talking head to have human-like behavior, this linkage should be emulated. One way to do that is to estimate the articulatory features from a given utterance and to use them to control a talking head. In this paper, we describe an algorithm to estimate the articulatory features from a spoken sentence using a novel computational model of human vocalization. Our model uses a set of fuzzy rules and genetic optimization. That is, the places of articulation are considered as fuzzy sets whose degrees of membership are the values of the articulatory features. The fuzzy rules represent the relationships between places of articulation and speech acoustic parameters, and the genetic algorithm estimates the degrees of membership of the places of articulation according to an optimization criteria and it performs imitation learning. We verify our model by performing audio-visual subjective tests of animated talking heads showing that the algorithm is able to produce correct results. In particular, subjective listening tests of artificially generated sentences from the articulatory description resulted in an average phonetic accuracy slightly under 80%. Through the analysis of large amounts of natural speech, the algorithm can be used to learn the places of articulation of all phonemes of a given speaker. The estimated places of articulation are then used to control talking heads in humanoid robotics.

Among social infrastructure technologies, Robot Technology (RT) is expected to play an important role in solving the problems of the aging society. New generations of personal robots are expected to be capable of assisting humans in a variety of contexts and thus of interacting and communicating with them effectively and even in a friendly and natural way. Expressing human-like emotions is an important capability to this aim. The objectives of this work are the design and development of two five-fingered robotic hands for a humanoid upper body able to generate and express emotions. The specific design goals have been grasping and expression of emotions through hand gestures, as a complement to facial expression of emotions. The paper presents the design process of the robotic hands, named RCH-1 (Robocasa Hand No. 1), starting from the requirements deriving from their use in grasping and gestures. The resulting robotic hands are described in detail, together with the hand sensory systems. Experimental trials are then presented, aimed at assessing the hand performance and at validating their effectiveness in grasping and emotion expression, when mounted on the emotion expression humanoid robot WE-4R (Waseda Eye No. 4 Refined).