A walking pattern generation method of humanoid robot MAHRU-R

This paper proposes an omni-directional walking pattern generation method for a humanoid robot MAHRU-R. To walk stably without falling down, a humanoid robot needs the walking pattern. Our previous walking pattern method generated the walking pattern with linear polynomials of the zero moment point (ZMP). It implemented the simple walking like forward/backward walking, side step walking and turning. However, this method was not sufficient to satisfy the various walking which is combined by forward/backward walking, side step walking and turning. We needed to upgrade the walking pattern generation method to implement an omni-directional walking. We use the linear inverted pendulum model consisted of ZMP and center of mass in order to simplify the computation of walking pattern. The proposed method assumes that the state of the following stride is same to the state of the current stride. Using this assumption of walking pattern, the proposed method generates the stable walking pattern for various walking. And the proposed scheme generates the ZMP trajectory with the quartic polynomials in order to reduce the fluctuation of ZMP trajectory by various walking. To implement the efficient walking pattern, this method proposes three walking modules: periodic step module, transient step module and steady step module. Each step module utilizes weighted least square method with future ZMP position information. The effectiveness of the proposed method is verified by simulations of various walking. And the proposed method is confirmed by the experiment of real humanoid robot MAHRU-R.     

Human-like natural behavior generation based on involuntary motions for humanoid robots

Human behaviors consist of both voluntary and involuntary motions. Almost all behaviors of task-oriented robots, however, consist solely of voluntary motions. Involuntary motions are important for generating natural motions like those of humans. Thus, we propose a natural behavior generation method for humanoid robots that is a hybrid generation between voluntary and involuntary motions. The key idea of our method is to control robots with a hybrid controller that combines the functions of a communication behavior controller and body balancing controllers. We also develop a wheeled inverted pendulum type of humanoid robot, named “Robovie-III”, in order to generate involuntary motions like oscillation. By applying our method to this robot and conducting preliminary experiments, we verify its validity. Experimental results show that the robot generates both voluntary and involuntary motions.     

Motion planning for humanoid robots based on virtual constraints extracted from recorded human movements

In the field of robotics there is a great interest in developing strategies and algorithms to reproduce human-like behavior. In this paper, we consider motion planning for humanoid robots based on the concept of virtual holonomic constraints. At first, recorded kinematic data of particular human motions are analyzed in order to extract consistent geometric relations among various joint angles defining the instantaneous postures. Second, a simplified human body representation leads to dynamics of an underactuated mechanical system with parameters based on anthropometric data. Motion planning for humanoid robots of similar structure can be carried out by considering solutions of reduced dynamics obtained by imposing the virtual holonomic constraints that are found in human movements. The relevance of such a reduced mathematical model in accordance with the real human motions under study is shown. Since the virtual constraints must be imposed on the robot dynamics by feedback control, the design procedure for a suitable controller is briefly discussed.     

HOJO-brain for motion control of robots and biological systems

The purpose of this research was to propose and develop a control method in the robotic and biomedical fields which is configured by a robotic/biological simulator, an analytical control frame which has phase sequences, sensory feedback, and an artificial central pattern generator (CPG) which is constructed by a recurrent neural network (RNN) and a genetic algorithm (GA). We call such a controller a “HOJO-brain”, which means a supplementary brain for motion control. We applied this method in the robotic and biomedical fields. In the robotic field, the HOJO-brain was applied to a 5-DOF legged-locomotion robot and a 32-DOF humanoid simulation model consisting of antagonistic muscles. In the biomedical field, it was applied to animals as the FES (functional electrical stimulation) controller. This FES control system with a HOJO-brain has the potential to give more effective and emergent motion control to severely physically handicapped people such as quadraplegics. With computer simulations and simple experiments using animals, we abtained performance indices which confirmed the fine adaptability and emergence for motion control. This work was presented, in part, at the Second International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1997     

The generation of animated motion by means of a recurrent neural network

The possibility to use neural networks to guide animated motion sequences is investigated. The performance of two recurrent architectures, both derived from the cascade-correlation network, is compared. These architectures only differ in the objective function used to train the hidden units. Small differences in performance were observed, but both networks could successfully produce simple motion sequences. An animation environment was created to display arm movement and walking sequences.     

An efficient motion generation method for redundant humanoid robot arms based on motion continuity

This paper proposes a novel method of motion generation for redundant humanoid robot arms, which can efficiently generate continuous collision-free arm motion for the preplanned hand trajectory. The proposed method generates the whole arm motion first and then computes the actuators’ motion, which is different from IK (inverse kinematics)-based motion generation methods. Based on the geometric constraints of the preplanned trajectory and the geometric structure of humanoid robot arms, the wrist trajectory and elbow trajectory can be got first without solving inverse kinematics and forward kinematics. Meanwhile, the constraints restrict all feasible arm configurations to an elbow-circle and reduce the arm configuration space to a two-dimension space. By combining the configuration space and collision distribution of arm motion, collision-free arm configurations can be identified and be used to generate collision-free arm motion, which can avoid unnecessary forward and inverse kinematics. The experiments show that the proposed method can generate continuous and collision-free arm motion for preplanned hand trajectories.     

Trunk motion control during the flight phase while hopping considering angular momentum of a humanoid

In previous studies, various stabilizing control methods for humanoids during the stance phase while hopping and running were proposed. Although these methods contribute to stability while hopping and running, it is possibility that the control during the flight phase could also affect the stability. In this study, we investigated whether the control during the flight phase can affect the stability of a humanoid while running. To achieve stable hopping, we developed a control system that accounts for the angular momentum of the whole body during the flight phase. In this system, the angular momentum generated by the motion of the lower body in each time interval is calculated during the flight phase, and the trunk joints are controlled to generate the angular momentum necessary to compensate for the deviation of the waist posture, which is used as the reference point for the motion coordinate system of the robot. Once the proposed control system was developed and simulated, we found that the hopping duration in the unconstrained state was extended.     

Towards cooperation of heterogeneous,autonomous robots: A case study of humanoid and wheeled robots

In this paper a case study of the cooperation of a strongly heterogeneous autonomous robot team, composed of a highly articulated humanoid robot and a wheeled robot with largely complementing and some redundant abilities is presented. By combining strongly heterogeneous robots the diversity of achievable tasks increases as the variety of sensing and motion abilities of the robot system is extended, compared to a usually considered team of homogeneous robots. A number of methodologies and technologies required in order to achieve the long-term goal of cooperation of heterogeneous autonomous robots are discussed, including modeling tasks and robot abilities, task assignment and redistribution, robot behavior modeling and programming, robot middleware and robot simulation. Example solutions and their application to the cooperation of autonomous wheeled and humanoid robots are presented in this case study. The scenario describes a tightly coupled cooperative task, where the humanoid robot and the wheeled robot track a moving ball, which is to be approached and kicked by the humanoid robot into a goal. The task can be fulfilled successfully by combining the abilities of both robots.     

Can a humanoid robot continue to draw attention in an office environment?

This study focuses on the use of humanoid robots in office environments, and investigates whether a robot can maintain the attention of passersby after initiation of face-to-face contact. Drawing attention can be considered as a first step in improving the continuity of use of robots; such continuity is one factor in validating their social acceptance, which must be considered when disseminating robots in offices. In this study, we assume that the robot approaches and greets users in order to make the users aware of its presence and encourage them to use it. In particular, the robots used in this study convey various greetings along with three nonverbal indicators (no motion, random motion, and face-to-face contact) when a passerby at the office is close to the robot. For a one-week period, we validated the social acceptance of the robot by examining how these robot motions influenced the rate and continuity of a passerby's attention. The results revealed that face-to-face contact can draw a high degree of attention, and that the presence of the robot affects the continuity with which attention is drawn. Finally, the paper discusses implications for future robot design, in terms of drawing and maintaining high rates of attention from users.     

A collaborative virtual geographic environment based on P2P and Grid technologies

Solving a geographic problem usually requires collaborative work among a group of people in different geographic locations. Collaborative virtual geographic environment (CVGE), an integrated technology, offers an intuitive, efficient, and interactive visualization environment through which geographically separated users can explore complicated spatial information and conduct collaborative work. In this paper, two new technologies, Peer-to-Peer (P2P) and Grid computing, are tightly coupled to develop a CVGE system. This paper evaluates the potential contributions of the P2P and Grid technology to CVGE systems. Using a Grid based system architecture efficiently integrates and shares geographically distributed resources as well as modeling procedures built on different platforms. To offer a shared and interactive virtual collaborative geographic environment for resolving geographic problems, we developed several P2P services including a terrain visualization collaboration service and a video collaboration service. Finally, a CVGE prototype system is implemented for collaboration on silt dam planning on the Loess plateau. The experimental results show that the scheme developed in this paper is efficient and feasible.     

Multi-level cognitive machine-learning based concept for human-like “artificial” walking: Application to autonomous stroll of humanoid robots

We propose a machine-learning based multi-level cognitive model inspired from early-ages’ cognitive development of human’s locomotion skills for humanoid robot’s walking modeling. Contrary to the most of already introduced works dealing with biped robot’s walking modeling, which place the problem within the context of controlling specific kinds of biped robots, the proposed model attends to a global concept of biped walking ability’s construction independently from the robot to which the concept may be applied. The chief-benefit of the concept is that the issued machine-learning based structure takes advantage from “learning” capacity and “generalization” propensity of such models: allowing a precious potential to deal with high dimensionality, nonlinearity and empirical proprioceptive or exteroceptive information. Case studies and validation results are reported and discussed evaluating potential performances of the proposed approach.     

Recommenders in a personalized,collaborative digital library environment

We envisage an information source not only as an information resource where users may submit queries to satisfy their daily information need, but also as a collaborative working and meeting space of people sharing common interests. Indeed, we will present a highly personalized environment where not only users may organize (and search into) the information space according to their individual taste and use, but which provides advanced features of collaborative work among the users. It is up to the system to discover interesting properties about the users’ interests, relationships between users and user communities and to make recommendations based on preference patterns of the users, which is the main topic of this paper.     

Evolutionary control of bipedal locomotion in a high degree-of-freedom humanoid robot: first steps

This article describes a methodology, together with an associated series of experiments employing this methodology, for the evolution of walking behavior in a simulated humanoid robot with up to 20 degrees of freedom. The robots evolved in this study learn to walk smoothly in an upright or near-upright position and demonstrate a variety of different locomotive behaviors, including “skating,” “limping,” and walking in a manner curiously reminiscent of a mildly or heavily intoxicated person. A previous study demonstrated the possible potential utility of this approach while evolving controllers based on simulated humanoid robots with a restricted range of movements. Although walking behaviors were developed, these were slow and relied on the robot walking in an excessively stooped position, similar to the gait of an infirm elderly person. This article extends the previous work to a robot with many degrees of freedom, up to 20 in total (arms, elbows, legs, hips, knees, etc.), and demonstrates the automatic evolution of fully upright bipedal locomotion in a humanoid robot using an accurate physics simulator. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

Examining different types of collaborative learning in a complex computer-based environment: A cognitive load approach

This study compared the effects of two collaborative learning strategies (Open-ended and Task-based) with an individualized learning strategy on individual learning in a computer-based environment. The experiment sought ecological validity by conducting it under real teaching and homework conditions. Ninety-four students from grade 9 participated in a webpage design task. Cognitive load theory was used to predict that the collaborative approaches would outperform the individualized approach due to reduced cognitive load. This hypothesis was confirmed by performance scores and cognitive load only in the case of the Open-ended collaborative learning condition. Evidence was also found that the Open-ended collaborative learning condition outperformed the Task-based collaborative one. It was concluded that in collaborative learning a more Open-ended task design together with moderate independent sub-task requirements leads to more effective learning.     

Mediating the expression of emotion in educational collaborative virtual environments: an experimental study

The use of avatars with emotionally expressive faces is potentially highly beneficial to communication in collaborative virtual environments (CVEs), especially when used in a distance learning context. However, little is known about how, or indeed whether, emotions can effectively be transmitted through the medium of a CVE. Given this, an avatar head model with limited but human-like expressive abilities was built, designed to enrich CVE communication. Based on the facial action coding system (FACS), the head was designed to express, in a readily recognisable manner, the six universal emotions. An experiment was conducted to investigate the efficacy of the model. Results indicate that the approach of applying the FACS model to virtual face representations is not guaranteed to work for all expressions of a particular emotion category. However, given appropriate use of the model, emotions can effectively be visualised with a limited number of facial features. A set of exemplar facial expressions is presented.     

Digital twins for collaborative robots: A case study in human-robot interaction

Human-robot collaboration (HRC) can expand the level of automation in areas that have conventionally been difficult to automate such as assembly. However, the need of adaptability and the dynamics of human presence are keeping the full potential of human-robot collaborative systems difficult to achieve. This paper explores the opportunities of using a digital twin to address the complexity of collaborative production systems through an industrial case and a demonstrator. A digital twin, as a virtual counterpart of a physical human-robot assembly system, is built as a ‘front-runner’ for validation and control throughout its design, build and operation. The forms of digital twins along system's life cycle, its building blocks and the potential advantages are presented and discussed. Recommendations for future research and practice in the use of digital twins in the field of cobotics are given.     

Online generation and control of quasi-static multi-contact motion by PWT Jacobian matrix with contact wrench estimation and joint load reduction*

In this paper, we propose a generic method of online motion generation and control that realizes quasi-static multi-contact motion for real position-controlled humanoids. The proposed system calculates command joint angle online by prioritized inverse kinematics that realizes the target contact states and the target position/orientation of interaction end-effectors feasibly. In order to enable control of contact wrench and joint torque with position-controlled robots, Jacobian matrixes (PWT Jacobian matrix) focusing on relationships between command joint angle and actual joint position, contact wrench, and joint torque are introduced. In addition, contact wrench is estimated at the body parts where force sensors are not mounted to enable contacts there, and joint load reduction based on the motor temperature is taken into account to enable long-term motions with real humanoids. The proposed method was verified by a real life-size position-controlled humanoid HRP2-JSKNTS, and various multi-contact motions such as desk climbing using the right knee and both arms were realized.     

Development of a distributed collaborative design framework within peer-to-peer environment

Distributed collaborative design and manufacture enables manufacturing organizations to maintain competitiveness in the fiercely competitive global industry. This requires that the distributed system not only maintains data consistency across globally-distributed locations seamlessly, but also allows team members to access the storage system and computing resources transparently and securely. Current grid applications mainly based on client-server architecture are inflexible and rigid for fast changing collaborations among manufacturers, especially for small and medium enterprises. In this paper, a distributed collaborative design framework is presented with a hybrid of grid and peer-to-peer technology. In order to access computational resources for design, analysis and process simulation, a meta-scheduler is designed and implemented. It helps in resource discovery and optimal utilization of resources. A test bed is established, based on the framework proposed to demonstrate a distributed collaborative design and manufacturing environment.