Detecting subtle fault signatures in vibration signals, masked by intense, non-Gaussian noise, poses a major challenge for the early diagnosis of bearing faults. This paper presents a novel multikernel correntropy transfer robust dictionary learning (MKC-TRDL) framework designed to address the challenges in bearing fault diagnosis. MKC-TRDL incorporates a multikernel correntropy-based data fidelity term, specifically crafted to minimize the impact of outliers, thereby ensuring more robust fault feature extraction. Furthermore, a transfer regularization term is introduced to guide the target dictionary to remain closely aligned with the source dictionary, striking an effective balance between preserving general signal features and adapting to the specific operating conditions of the bearing. This approach significantly enhances the robustness of the approach and its capacity to perform reliably in dynamic and noisy environments. Simulations and experimental results show that the MKC-TRDL method effectively extracts early bearing fault features, particularly in the presence of strong complex noise.

This paper suggests the sequential inverse optimal control (SIOC) approach for discrete-time system. It determines the unknown weight vectors of the cost function in real-time utilizing the input and output of a discrete-time system that is optimally controlled. It systematically calculates the possible solution spaces and their intersections until the intersection space's dimension is reduced to one. The remaining one-dimensional vector at the intersection of the possible solution space is the solution to the IOC problem. In this method, we clarify the conditions on the decrease of the dimension of the intersection space follow with the tackling method of noisy data. The simulation results illustrate the high calculation speed and effective noise-tackling results of our method.

Abstract Given the challenge of pear leaf disease recognition caused by uneven illumination, overlapping leaves, and other green plants in the background, a two‐stage strategy‐based framework for pear leaf segmentation and disease classification is proposed. Initially, a double branch polymerization net fusing features of the low‐level feature branch and semantic branch is constructed to extract the target diseased pear leaf and eliminate background interference. Then an improved lightweight neural network (Inverted‐Inception Efficient‐Excitation‐and‐Filtering‐Bottleneck MobileNet‐v2, I 2 EMv2) is used to capture multi‐scale lesion information and ainhibit invalid feature channels and filter redundant features for the final classification. The experimental results show that the proposed framework can accurately extract the pear leaf region with complete boundary from the complex background, maximize the retention of lesion information, and achieve high‐precision pear leaf disease identification. The mean absolute error, F‐measure, and intersection over union of leaf segmentation are 0.027, 0.947, and 0.880, respectively, and the average recognition accuracy of leaf disease is 92.05%. Compared with others, the method proposed in this paper has superior performance on segmentation and classification, which provides a reference for pear leaf disease classification under complex background.

Domain terminology recognition and extraction is the primary work for the construction of domain knowledge graph. Traditional method is tedious, and time-consuming, as well as low accuracy. This paper presents an improved Domain Term Extraction-Improvement (TDE-I) method based on relative co-occurr ence rate, which can automatically extract domain basic terminologies and domain compound terminologies. We also present a Document Classification Value (DCV) method on the basis of calculating Domain Feature Vector (DFV) value which can implement a judgment to evaluate extraction accuracy according to the evaluation indexes. Our experimental results demonstrate that our approach is effectiveness and accuracy. The proposed method can provide with a new solution for the construction of domain knowledge graph and its applications.

In order to defend adversarial attacks in computer vision models, the conventional approach arises on actively incorporate such samples into the training datasets. Nonetheless, the manual production of adversarial samples is painful and labor intensive. Here we propose a novel generative model: Convolutional Autoencoder Model to add unsupervised adversarial training, i.e., the production of adversarial images from the encoded feature representation, on conventional supervised convolutional neural network training. To accomplish such objective, we first provide a novel statistical understanding of convolutional neural network to translate convolution and pooling computations equivalently as a hierarchy of encoders, and sampling tricks, respectively. Then, we derive our proposed Convolutional Autoencoder Model with the `adversarial decoders' to automate the generation of adversarial samples. We validated our proposed Convolutional Autoencoder Model on MNIST dataset, and achieved the clear-cut performance improvement over the normal Convolutional Neural Network.

As the computer vision and virtual reality (VR) technology become mature nowadays, there is a huge potential to connect VR with the real world. In this paper, a 3D interactive VR system with adaptive treadmill control is developed. The subject walks on the treadmill at will. Interaction force between feet and treadmill are measured to estimate the subject's intended walking speed. The treadmill belt velocity and 3D display adjust to the walking pace according to the subject's walking intention, which makes the subject feel that he (or her) is walking in the real market. The experiment shows that the control results are smooth, which verifies the validity of the whole system.

Period-doubling bifurcation and chaotic behavior are interesting phenomena in limit cycle walking. Their mechanisms are very complicated and their roles in dynamic biped locomotion are still unclear. This paper then investigates how the gait efficiency changes with the asymmetrization through analysis of a simple rimless wheel model. We mathematically show that the symmetric configuration gives the optimal solution when the mean value of the inter-leg angles is constant in terms of kinetic energy. This is derived from two magnitude relations of the energy-loss coefficient and restored mechanical energy. We also show that the symmetric gait is not always optimal from another viewpoint.

Mobile robot could navigate in unknown environment autonomously with the help of simultaneous localization and mapping (SLAM). Recently, SLAM based on information matrix enjoys much popularity since it is naturally sparse. However, the computational burden related to information matrix balloons with respect to the increase of the mapped landmarks. In this paper, by considering the features of information matrix, we present a novel method which wipes off nearly half of the elements in information matrix. The errors that come from sparsification decrease apparently by loop-closure. Furthermore, the relationship between sparsification and SLAM accuracy is analyzed theoretically. A large scale simulation and experiment conducted on a real robot suggest that the technique is effective for a robot's SLAM in real-world applications.

This paper reports some interesting results on our experimental study of parametrically excited dynamic bipedal walking. We describe the details of the walking machine that has telescopic legs, semicircular feet, free hip-joint and counterweights. The walker can sustain stable dynamic walking on level ground based on mechanical energy restoration in accordance with the principle of parametric excitation utilizing the effects of semicircular feet and counterweights. Results of numerical analysis of the effect of the counterweights on the gait efficiency are also described.

This paper studies the efficiency and asymptotic stability of a dynamic bipedal gait with a constraint on the impact posture. First, we generate a gait by using tracking control to achieve the desired trajectory of the hip-joint angle, and show that there is a trade-off between efficiency and robustness through a numerical simulation. Second, we investigate the asymptotic stability of the gait from the mechanical energy balance viewpoint, and discuss the importance of the control input properties. Furthermore, we point out that there is a feedback in mechanical energy in the discrete walking system, and it is difficult to detect a stable 2-period gait.

Achieving energy-efficient dynamic walking has become one of the main subjects of research on robotic bipedal locomotion. Approaches based on passive-dynamic walkers can accomplish bipedal locomotion. However, passive dynamic walking has only been studied with the legs, and the effect of an upper body has not been clarified. This paper investigates the effect of an upper body on the efficiency and stability of dynamic bipedal locomotion based on observations. We first investigated a suitable upper body, which was a simple 1-link torso with a bisecting hip mechanism that would not destroy natural dynamics of the biped model. Second, we analyzed the robot's driving mechanism and chose underactuated virtual passive dynamic walking as the method for generating an efficient dynamic gait. We confirmed that efficient dynamic walking was possible with a specific resistance of 0.01 and investigated the effect of the physical parameters of the upper body through numerical simulations.

This paper proposes an iterative learning control method for simultaneous force/position tracking tasks by using a 5 D.O.F. robotic thumb under non-holonomic rolling constraints. In our previous works, "blind touching", which is defined as a point-to-point control scheme for the robot to realize a desired contact position and a contact force simultaneously without any external sensing, have proposed. In this paper, an iterative learning control manner to realize a desired continuous trajectory of the center of the contact point together with a desired contact force on the task plane is proposed. The usefulness of this learning control method is demonstrated by showing results of computer simulations.

This paper shows through faithfully deriving Paffian forms of 3-D rolling contact constraints that 3-D pinching can be stabilized by using a pair of robot fingers with a hemispherical soft tip and minimum degrees of freedom under the gravity effect. The proposed control input is based on fingers-thumb opposition without using object information or external sensing. Stability analysis of the closed-loop dynamics is presented by using a Lyapunov method. Finally, for the sake of confirmation of effectiveness of the proposed control signals, numerical simulations are carried out.

This paper studies efficiency and symmetry of ballistic gait. We introduce a compass-like biped model and propose a simple control law for generating the dynamic gait. The efficiency is then numerically analyzed and the optimality of mechanical energy restoration is discussed. It is also shown that perfectly ballistic gait by kick action achieves the highest walking speed and exhibits symmetric gait automatically. We discuss how the symmetric gait is generated from the angular momentum point of view.

This paper investigates the effect of an upper body on efficient dynamic bipedal walking utilizing its natural dynamics. We introduce an upper body as a one-link torso and add it to a simple biped model by means of a bisecting hip mechanism (BHM). We first mathematically analyze the effect of the upper body with the BHM as a counterweight and discuss how it affects natural swinging motion of the swing leg. Second, we propose a simple method for generating efficient dynamic bipedal gait imitating the property of virtual passive dynamic walking, and numerically analyze the gait efficiency. Simulation results show that the walking system exhibits period-doubling bifurcation, and we discuss how the efficiency changes in the multiple-period gait.

For stable gait generation on the level floor, restoring mechanical energy lost by heel-strike collision at the ground is necessary. Parametric excitation principle is one of the solutions. Harata et al. applied this principle to a knee actuated robot to achieve parametric excitation by bending and extending the knee, and succeeded in generating a sustainable biped gait by computer simulation. Although only the dynamics of the swing-leg was considered in the method, the stance-legpsilas dynamics also affects the swing-legpsilas dynamics. Therefore we considered the whole dynamics of the robot in this paper. Therefore, we dealt with the robotpsilas center of mass as an inverted pendulum and achieved stable gait generation by parametric excitation of the inverted pendulum. The resultant energy efficiency was increased significantly.

Ionic polymer-metal composite (IPMC) actuators are expected as artificial muscles for bio-mimetic soft robots. The IPMC gel first bends quickly and then bends back slowly when a constant voltage is applied to the sample. The latter phenomenon, referred to as the stress relaxation, can be modeled by electrostress diffusion coupling theory. The theory can predict the behavior of the actuator for the different species of counterions. In order to describe the actuator dynamics as a system with inputs and outputs, this paper proposes a linear state space model based on the electrostress diffusion coupling theory. The model consists of the three systems, the electrical, the electro-mechanical, and the mechanical systems. The results of the simulation and the experiment demonstrate the validity of the model.

We clarified that the common necessary condition for generating a dynamic gait results from the requirement to restore mechanical energy through studies on passive dynamic walking mechanisms. This paper proposes a novel method of generating a dynamic gait that can be found in the mechanism of a swing inspired by the principle of parametric excitation using telescopic leg actuation. We first introduce a simple underactuated biped model with telescopic legs and semicircular feet and propose a law to control the telescopic leg motion. We found that a high-speed dynamic bipedal gait can easily be generated by only pumping the swing leg mass. We then conducted parametric studies by adjusting the control and physical parameters and determined how well the basic gait performed by introducing some performance indexes. Improvements in energy efficiency by using an elastic-element effect were also numerically investigated. Further, we theoretically proved that semicircular feet have a mechanism that decreases the energy dissipated by heel-strike collisions. We provide insights throughout this paper into how zero-moment-point-free robots can generate a novel biped gait.

Human-interactive robots, such as those used for nursing, which share humans' environments and interact with them, should be covered with soft areal tactile sensors for safety and dexterous manipulation. We report the successful development of the tactile sensor system of our human-interactive robot named RI-MAN, which can lift up a dummy human.

Realization of an energy-efficient and high-speed dynamic walking has come to be one of the main subjects in the research area of robotic biped locomotion, and passive dynamic walking has been widely attracted as a clue to solve the problem. It has been empirically known that the effect of convex curve shape of foot, which characterizes passive-dynamic walkers, is important to increase walking speed. This paper then investigates the driving mechanism of compasslike biped robots and the rolling effect of semicircular feet are mainly investigated. We first analyze the mechanism of a planar fully-actuated compass-like biped model to clarify the importance of ankle-joint torque introducing generalized virtual gravity concept. In the second, a planar underactuated biped model with semicircular feet is introduced and we show that virtual passive dynamic walking by hip-joint torque only can be realized based on the rolling effect. We then compare with a flat feet model through linear approximation, and show that the rolling effect is equivalent to its virtual ankle-joint torque. Throughout this paper, we provide novel insights into how ZMP-free robots can generate a dynamic bipedal gait.

This paper investigates the effect of semicircular feet on dynamic bipedal walking. It has been clarified by Asano and Luo (2006) that underactuated virtual passive dynamic walking can be realized by using the rolling effect, which acts as the ankle-joint torque virtually. It has been also shown that, throughout parameter studies, the rolling effect dramatically increases the stable domain of limit cycles. Now that the effect of semicircular feet during stance phase has been discussed, this paper then focuses the effect on mechanical energy dissipation by heel-strike. It is theoretically clarified that, through modeling and analysis of an inelastic collision, increasing walking speed is achieved not by the rolling effect during stance phase but by the effect of reducing mechanical energy dissipation by heel-strike.

Recently, active researches have been performed to increase a robot's intelligence so as to realize the dexterous tasks in complex environment such as in the street or homes. However, since the skillful human-like task ability is so difficult to be formulated for the robot, not only the analytical and theoretical control researches but also the direct human motion mimetic approach is necessary. In this paper, we propose that to realize the environmental interactive tasks, such as human care tasks, it is insufficient to replay the human motion along. We show a novel motion generation approach to integrate the cognitive information into the mimic of human motions so as to realize the final complex task by the robot.

Human can pinch or grasp and manipulate an object stably and dexterously. Accomplishment of such tasks is contributed from human hand's configuration, called "fingers-thumb opposability". This opposability of the thumb against other digits is specific and granted to only human among primates. When we use a cell phone, or change a TV's channel using a remote controller, we grasp it by a palm and digits other than the thumb, and push buttons using the thumb quickly, without looking the buttons. These kinds of thumb's movement seem to be one of the most intelligent movements in a human. Therefore, execution of such touching tasks without visual or tactile sensing is called in this paper "blind touching". The goal of this research is to realize humanlike "blind touching" by means of a 5 D.O.F. thumb robot model with soft and hemispherical finger-tip. To do this, we formulate a simultaneous contact position and touching force control by using 3-dimensional rolling contact with the task plane. First, dynamics of the 5 D.O.F. thumb robot model with hemispherical soft finger-tip under rolling constraints is derived. Then, a sensory-motor control law without vision, force or tactile sensing is proposed. Some numerical simulations show that the desired contact position and touching force can be attained by the proposed control scheme. A theoretical proof of convergence to the desired state is also presented.

Traditionally, an inverted pendulum has been used as a reduced biped locomotion system, whereas this paper proposes a different approach. The essence of dynamic biped gait generation is mechanical energy restoration, and parametric excitation approach is a good idea for it. Our novel approach does not require any rotational actuation and thus enables to be free from the constraint of zero moment point (ZMP). This paper considers some basic methods of parametric excitation and shows that energy-efficient biped locomotion can achieved very easily without taking the ZMP condition into account. We then conduct parametric studies by adjusting the control and physical parameters, and determine how well the basic gait perform by introducing some performance indices.

This paper investigates dynamic bipedal gait and its stability from the mechanical energy balance point of view. The equilibrium points at impact in a dynamic gait are uniquely and systematically determined by two constraint conditions; one is the constraint on restored mechanical energy, the other is the constraint on impact posture. The dynamic gait then becomes always asymptotically stable around the equilibrium points, and this is shown by a simple recurrence formula of the pre-impact kinetic energy. The validity of the method is numerically confirmed via gait generation by virtual passive dynamic walking.

Restoring mechanical energy lost by heel-strike collisions is necessary for stable gait generation. One principle to realize this is parametric excitation. Recently, Asano et al. applied this principle to a biped robot with telescopic-legs, and succeeded in generating a sustainable biped gait by computer simulation. In this paper, we deal with a model of a biped robot that has not only semicircular feet but also actuated knees. Though this robot has no actuator at the hip, knee actuators can sustain gait by parametric excitation. We first verify that an actuated knee can cause parametric excitation, and then show by computer simulation that the proposed biped robot can walk continuously with actuated knees only.

Bucky gel actuator is a novel electro-active polymer (EAP), which is a low-voltage driven dry soft actuator. Its device has a bimorph structure with polymer-supported bucky gel electrodes and a polymer-supported ionic gel electrolyte. It can be fabricated by layer-by-layer casting, to form any shape easily. In addition, the bucky gel device generates electromotive force when bending, and then it also can be used as a sensor. Since sensor and actuator functions exist in a same device, flexible and miniature integrated actuator-sensor systems for soft robotics can be constructed easily. In this study, we conduct some experiments to verify the possibility of the bucky gel actuator/sensor. Feedback controls of the actuator are demonstrated and the characteristics of the sensor are investigated. By utilizing both functions of the actuator and the sensor, feedback control based on the sensor signal is demonstrated.

We constructed a snake-like swimming robot using IPMC actuator, and verified swimming motion based on numerical simulation and experiments. In applying periodic inputs with appropriate frequency and phase shift, the snake-like robot is capable of smooth propulsion. It is known that IPMC has a sensor function that IPMC films generate electromotive voltage when bending or being deformed. By using the sensor function into the snake-like robot, it is considered that autonomous propulsive motion can be realized by feedback of the sensor signal. In this paper, we consider the autonomous locomotion of the snakelike swimming robot with IPMC actuator/sensor, and verify the realization of swimming motion by feedback of the sensor signal. Furthermore, the efficiency of the autonomous locomotion is investigated

In this paper, we study co-activation of digitorum muscles while perform stable pinching and posture regulation tasks of an object by using dual fingers. The fingers have 2 D.O.F. joints and are actuated by nonlinear redundant digitorum muscles to mimic human-like pinching movements. Firstly, we illustrate the kinematics and the dynamics of the overall system, which consider not only the fingers and an object, but also three muscles for each finger to actuate the finger links. Secondly, we consider nonlinear muscle property based on several physiological studies, and propose sensory-motor control rule to the muscles in order to realize stable pinching simultaneously with posture regulation by introducing internal force term induced by co-activation between flexor digitorums and extensor digitorums to modulate the damping factor in joint space. We verify our study by numerical simulations and conclude that this dual fingers system can realize human-like stable pinching and posture regulation

We are studying about robotic application of ionic polymer-metal composite (IPMC). The characteristics of IPMC highly depend on the type of counter-ions, and it is considered that the performance of the actuators can be improved by combining the actuators with several types of counter-ions and applying an integrated control. IPMC has also a sensor function, as the IPMC film generates electromotive force when it is deformed. It has possibility to be integrated into IPMC actuator with soft actuation. In this paper, we consider an integrated design of IPMC actuator/sensor, and investigate a control of the combined IPMC actuators using Hinfincontrol and the construction of IPMC sensor system

This paper investigates effectiveness of semicircular feet on dynamic biped locomotion. We first introduce the simplest biped model with semicircular feet and show its level walking by hip-joint actuation. In the second, parameter study is performed by adjusting the physical and control parameters. Throughout numerical analysis, it is shown that the semicircular feet dramatically increases the walking speed and the stable domain. By the effect of the rolling, energy-efficient and high-speed dynamic biped locomotion on a level can be realized easily without ankle-joint actuation nor concerning the zero moment point condition

Our goal is to create advanced engineering systems such as a soft human interactive robot. The robot developed here is named RI-MAN. RI-MAN exhibits the skill and ability to realize human care and welfare tasks. RI-MAN can search out a specific person in real time by fuing audio and visual information, and understand human speech based on a sound recognition function. In addition, RI-MAN's body is coverd with soft touch sensors, and RI-MAN can react to the amplitude and location of external forces. Using all these sensor functions, RI-MAN can successfully follow human commands and hold up a dummy of the same size as an adult human. RI-MAN will become an invaluable partner robot.

The paper presents an analysis of human reaching movements in the manipulation of flexible objects. Two models, the minimum hand jerk and the minimum driving force-change, are derived and their basic features are analyzed. It is shown that the first model features two-phased hand velocity profiles, while in the second models there are multiple phases. The analysis of the phase transitions for the models considered is done in the analytical form. The results of this analysis can be helpful in the design of experimental scenarios for the verification of the theoretical models. Finally, we present some initial experimental results and analyze the applicability of the models developed in this paper

This paper focuses on a sensory-motor control mechanism in human reaching movements from the perspective of robotics. By formulating a musculo-skeletal redundant system which takes into account a nonlinear muscle property and performing numerical simulations, we suggest that the human-like reaching movements can be realized by using only simple task-space feedback scheme together with the internal force effect coming from nonlinear property of muscles without any complex mathematical computation such as an inverse dynamics or some optimal trajectory derivation. Firstly, we introduce both kinematics and dynamics of a three-link serial manipulator with six monoarticular muscles and three biarticular muscles model whose movements are limited within a horizontal plane. Secondly, the nonlinear muscle property coming from a physiological study based on Hill's muscle model, is taken into consideration. This nonlinearity makes it possible to modulate the damping effect in joint-space by considering the internal force generated by the redundant muscles. By utilizing this feature, the end-point converges to the desired point using only simple task-space feedback control scheme, even thought the system owns both the joint and muscle redundancies. Finally, we illustrate numerical simulations to show the effectiveness of the control scheme, and suggest one of the direction to study brain-motor control mechanism of human movements

Ionic polymer-metal composite (IPMC), which is one of the electro-active polymer actuators, is expected as artificial muscles for robots. An interesting property of IPMC is that it requires water to work, therefore it is suitable for underwater robots. In this paper, we developed an underwater robot which mimics rajiform swimming, that is the swimming form of a ray fish. Fins are designed using sixteen IPMCs. For autonomous operation, miniaturization of the electrical devices such as a micro controllers and small amplifiers are performed. A simple traveling wave control input is employed to generate moment on the fin. In the experiment, propulsion speed is measured under various control parameters. Furthermore, incremental wave of the fin is observed although the amplitude of the control input is spatially uniform. We also discuss this phenomenon from the point of view of interaction between elasticity of the actuator and fluid dynamics

The paper presents an analysis of human reaching movements in manipulation of flexible objects. To predict the trajectory of human hand, a minimum crackle criterion has been recently introduced in literature. A different approach is explored in this paper. To explain the trajectory formation, we resort to the minimum hand jerk criterion. First, we show that this criterion matches well experimental data available in literature. Next, we argue that, contrary to the minimum crackle criterion, the minimum hand jerk criterion produces bounded hand velocity profiles for multimass flexible objects. Finally, we present initial experimental results confirming the applicability of the minimum hand jerk criterion to the prediction of reaching movements with multimass objects.

It is already clarified throughout studies of passive dynamic walking mechanisms that the common nec essary condition for dynamic gait generation comes from the requirement on mechanical energy restoration. Until now we have treated only rotational joints of the robot, whereas in this paper we consider a novel dynamic gait generation method based on mechanical energy restoration by parametric excitation using telescopic leg actuation. We first introduce a simple walking model and a control law for the telescopic leg motion, and show the typical walking pattern by numerical simulations. We then analyze the gait performance by adjusting some control and physical parameters. In addition, some extensions of the mechanism and control applications are investigated.

In telepresence, it is critical for the local user to control the remote agent’s movement through his own locomotion in order to ensure a high degree of realism. Since the local user’s environment is normally different from that of the remote agent, there exists a motion mapping from the remote agent to the local user. After the path of the remote agent is predicted or recognized, it should be transformed to fit into the local environment, considering the constraints from the local environment, and ensuring utmost similarities in the shape and length of the paths. Moreover, terminal position of the local user in the local environment after a piece of known movement should also be carefully arranged after path transformation for his consecutive motions. These issues are incrementally addressed from the optimization point of view. Two schemes are proposed for path transformation problem. Extensive simulations and comparisons show the feasibility and effectiveness of the proposed approaches.

The paper presents an analysis of human reaching movements in manipulation of flexible objects. To predict the trajectory of human hand, a minimum crackle criterion has been recently proposed in literature. A different approach is explored in this paper. To explain the trajectory formation, we resort to the minimum hand jerk criterion. First, we show that this criterion matches well experimental data available in literature. Next, we argue that, contrary to the minimum crackle criterion, the minimum hand jerk criterion produces bounded hand velocity profiles for multi-mass flexible objects. Finally, we present initial experimental results confirming the applicability of the minimum hand jerk criterion in manipulation of multi-mass objects.

It is well-known that a human musculo-skeletal body is redundant in terms of both kinematics and dynamics. The former means that the degree of freedom in joint space is larger than that in task space, and the latter means that a joint is driven by a number of muscles. All human skillful movements can be performed by using both redundancies. However, these redundancies induce the underlying ill-posedness problem that each joint angle and muscle’s output forces cannot be uniquely determined. These ill-posedness problems are known as “Bernstein’s problem” and are important to understand how human multi-joint movements are produced. In this study, we address the latter redundancy problem on how muscle’s output forces can be determined from the viewpoint of robotics. In this paper, we consider a reaching movement by means of a two-link planar arm with six muscles and show that both damping and elastic properties coming from nonlinear dynamics of the muscles play a crucial role. By using a simple task space feedback control input together with an additional term to control the internal force to regulate damping and elasticity in joint space, we show some simulation results which exhibit human-like quasi-straight line movement.

Constrained human movements are considered in this paper. The external constraints decrease the mobility of the human arm and lead to the redundancy in the distribution of the interaction force between the arm joints. To investigate the trajectory formation in the constrained human movements, we first develop a novel experimental system with interchangeable geometric constraints. Then, we examine the trajectory of human arm for an elliptic constraint. To clarify the trajectory formation in constrained point-to-point motions, we analyze experimental data and test them against predictions obtained by conventional criteria of optimality. It is found in the comparative analysis that the best prediction is given by the minimum muscle force change criterion.

In this paper, we study the sensory motor control mechanism in human reaching movements by considering the redundant muscle dynamics. We first formulate the kinematics and dynamics of a two-link arm model with six muscles, and introduce the nonlinear muscle dynamics based on the biological understanding. Secondly, we show the stability of the system by using intrinsic muscle characteristics and La Salle's invariance theorem. From this result and the numerical simulations, we propose that the reaching movement can be regulated by the internal forces of the redundant muscles, in detail the muscle's internal forces can be used to control the damping of the joints. In addition, human can compensate the gravity by using antigravity muscles. To realize this effect in the arm, we propose the gravity compensation method at the muscle input level from the viewpoint of robotics. We present the result of numerical simulation to verify the usefulness of this compensation method.

Ionic polymer metal composite (IPMC) is one of the most promising EAF actuators for applications, and slave good property of response and durability. The characteristics of IPMC materials depend on a type of counter ion. In applying to mechanical systems such as a robot, there exist possibilities to change the properties of the dynamics by changing the counter ions according to environment or purpose adequately. In this paper, we consider swimming of a snake-like robot with IPMC actuator, and demonstrate a smooth swimming motion. Then, we also verify the doping effects by experiments.

Principal mechanisms of passive dynamic walking are studied from the mechanical energy point of view, and novel gait generation and control methods based on passive dynamic walking are proposed. First, a unified property of passive dynamic walking is derived, which shows that the walking system's mechanical energy increases proportionally with respect to the position of the system's center of mass. This yields an interesting indeterminate equation that determines the relation between the system's control torques and its center of mass. By solving this indeterminate equation for the control torque, active dynamic walking on a level can then be realized. In addition, the applications to the robust energy referenced control are discussed. The effectiveness and control performances of the proposed methods have been investigated through numerical simulations.

This paper studies the dynamic principles of passive dynamic walking and proposes novel gait generation and control methods based on it. The authors have clarified a unified property of passive dynamic walking which shows that the walking system's mechanical energy increases proportionally with respect to the position of system's center of mass. Following this, the gait generation problem yields solutions of an indeterminate equation. By solving it for the control torque, active dynamic walking on a level can then be realized. In this paper, we first discuss the solutions and consider the unification of the previous gait generation methods from a variable virtual gravity point of view. Second, its applications to a kneed biped system considering ZMP condition and robust energy referenced control are discussed. The effectiveness of the proposed methods have been investigated through numerical simulations.

This paper studies modeling and bio-mimetic control of a 3D 8-dof whole arm cooperative manipulation system using sensitive skin. A sphere is considered here as a manipulated object. The control law is designed based on integration of voluntary and reflex movements considering the system's redundancy. The voluntary task for holding the object is realized by impedance control at the four contact points with the object using the contact force information from the sensitive skin. The reflection on the other hand is introduced as a regulation problem of the direction between the points of end-effectors and elbows. The solution for the redundant control is formulated and derived from the optimization point of view. The validity of the proposed method is investigated by numerical simulations.

The authors have clarified the mechanism of passive dynamic walking from the mechanical energy point of view, and reported its basic results. This paper then considers the generalization and unification of dynamic gait generation methods by introducing variable virtual gravity concept and some solution formulas as well as essential mechanical energy orbits. As two leading methods, energy tracking control and virtual passive dynamic walking are considered, and we analyze the control performances of robust stability and energy-efficiency criterions by numerical simulations. Finally we discuss the application possibility of the methods to actual walking machines from the ZMP point of view.

This paper proposes novel energy-based gait generation and control methods for biped robots based on an analysis of passive dynamic walking. First, we discuss the essence of dynamic walking using a passive walker on a gentle slope from the mechanical energy point of view. Second, we propose a simple and effective gait-generation method, which imitates the energy behavior in every walking cycle considering the zero-moment point condition and other factors of the active walker. The control strategy is formed by taking into account the features of mechanical energy dissipation and restoration. Following the proposed method, the robot can exhibit a natural and reasonable walk on a level ground without any gait planning and design in advance. The effectiveness of the method is examined through numerical simulations and experiments.

Opening a door, turning a steering wheel, rotating a coffee mill are typical examples of human movements constrained by the external environment. The constraints decrease the mobility of the human arm and leads to the redundancy in the distribution of the interaction force between the arm joints. Due to the redundancy of the force actuation in the constrained motions, there is infinite number of ways to form the trajectory of the arm. However, human forms the hand trajectory in a unique way. How does human resolve the redundancy of the constrained motions and specify the hand trajectory? To investigate these problems, we examine the trajectory of human arm in a crank rotation task. To explain the trajectory formation in constrained point-to-point motions, we formulate an optimal control problem and propose a novel criterion minimizing the hand contact force change and muscle force change over the time of movement. The simulation results are compared with human motion and force profiles obtained experimentally. It is shown that the novel criterion captures the characteristics of the human constrained motion much more satisfactory than conventional criteria accepted in the research community.

Unlike the present manipulator control technologies that operate objects only by the robots' end-effectors, human beings can perform whole body manipulation flexibly and easily. Bio-mimetic research of such skillful human motor behavior is important not only for deeper understanding of human sensations and nervous control functions but also for developing of higher level robots. In this paper, we first formulate the basic model of multipoints whole body interaction between a robot manipulator and its object. We then study three control approaches by considering the limitations of force feedback, the complexity of control algorithms. As an example, we consider a 2-link planar manipulator that operates a circle object with dynamic arm friction. We show a computer simulation algorithm, and compare the results of each control methods, numerically.

The paper deals with the problem of the trajectory formation in human movements constrained by the external environment. To investigate this problem, we examine reaching movements of the human arm in a crank rotation task. The experimental data show that after learning and accommodation the position, velocity, and the tangential force trajectories converge to unique profiles while that of the normal force do not. To explain the formation of the human trajectories observed in the experiments, we resort to the optimization approach. Different criteria of optimality are analyzed using simplified kinematic and dynamic models. From the kinematic analysis we suggested that in the process of learning and adaptation the brain constructs a suitable parameterization of the constraint manifold. In the dynamic analysis it is found that the human trajectories cannot not be captured by a single conventional criterion. It is shown that a weighted combination of the hand force changes and the joint torque changes produces better results but brings the problem of weight selection.

With the development of information science and robotic technology, it becomes more important to generate human interactive robots. The design platform for developing such robots should satisfy three basic conditions: (1) it can test safely the performance of the robot through the physical interaction with human, (2) human subject can estimate subjectively the outside appearance of the robot, and (3) it can simulate the dynamic human interactive robot motion within real-time. This paper proposes our immersion type dynamic simulation platform. An application to estimate the robots performance when performing cooperative object lifting task with human subject is chosen in order to show the effectiveness of our system. The analysis of the recorded data is useful to design the novel human interactive robots.

This paper proposes an adaptive modular vector field control (AMVFC) approach for a robot manipulator to interact with its uncertain environmental geometric constraints. Started from the uncertain geometric model of the environment, the approach first parameterizes the desired velocity vector field of the robot by the weighted combination of a set of basis vector fields. Then, to overcome the influences from the environmental model uncertainties, force feedback is added to adjust robot dynamics as well as the weight parameters of the desired velocity field for the robot to approach the real environment. Simulations of a robot interacting with an uncertain circle show the effectiveness of our approach.

Kinematics aspect of planning and control of human arm movements are considered in this paper. First, we analyze unconstrained reaching movements using simple analytical models. Here, we introduce a generalized minimum jerk criterion, analyze the smoothness of the optimal solutions and discuss the role of the boundary conditions in the trajectory formation. Next, we analyze the constrained human movements using a crank rotation task. Analysis of the experimental data shows that constrained and unconstrained reaching movements might involve different control strategies, which reflected in the choice of the optimality criterion. It is demonstrated that among the kinematics based criteria, the minimum crank jerk criterion produce a rough matching to the experimental data. It is hypothesized that in the process of learning and adaptation the brain constructs a suitable parameterization of the strained manifold.

Human hand can not only catch and grasp the complex objects but also easily manipulate the objects by switching various types of interactions. Research on the basic mechanics and control principles of hand's dexterous manipulation abilities is one of the most important subjects in bio-mimetics. In this paper, we formalize the multi-fingered hand manipulation problem as a general dynamic complementarity (DC) system. Based on the fact that the DC system can be transformed to a mixed logical dynamical (MLD) model, and that MLD system can control problem can be solved using powerful mixed integer programming (MIP) algorithm, we propose to realize dexterous hand manipulations by using a cyber-grasp system, and study its robotic realization on GIFU hand III, which is equipped with tactile sensors. We suggest that in order to obtain sub-optimal solutions, biologically inspired techniques might be very powerful.

An analysis of the stiffness of redundant manipulators is undertaken in this paper. First, the matrix of the force-dependent stiffness is derived and its basic properties are analyzed. In particular, in the planar case the stability conditions for the force dependent stiffness (and gravity-dependent stiffness) are obtained in the analytical form. Next, dual properties of the stiffness and compliance are exploited to establish a decomposition of the joint stiffness and compliance in the form similar to the decomposition of the joint velocities and torques. Finally, a minimal, nonredundant parameterization of the joint stiffness and compliance is commented.

In this paper, we propose a control algorithm for the problem of stabilization of Acrobat Robot in upright position on a horizontal bar, in an actual experimental environment. The dynamics of the closed loop is designed to match a stable closed loop dynamics around the equilibrium. Moreover, we apply an iterative learning control in order to deal with modeling errors. The validity of the proposed methods is shown by numerical simulations and experiments.

This paper proposes a novel energy-based control law for biped robots based on an analysis of passive dynamic walking. Firstly we discuss the essence of dynamic walking using a passive walker on a gentle slope. In the second, we propose a simple and effective control law which imitates the energy behavior in every cycle considering the ZMP condition and other factors of the active walker. The control strategy is formed by the feature of mechanical energy dissipation and restoration. By the effect of the proposed method, the robot can exhibit natural and reasonable walk on a level ground without any gait design in advance. The validity of the proposed method is examined by numerical simulations and experiments.

Deals with a crank rotation task. The task requires coordinated movements of arm links without developing excessive internal forces. This research is directed, mainly, to the understanding of comfortable human movements constrained by the external environment. To get a deeper insight into the crank rotation task, we develop a mathematical model and analyze a weighted minimum norm muscle force distribution scheme that can be used in the resolution of the force redundancy. Analysis of experimental data shows that in comfortable motions a human is likely to modulate the rotational stiffness of the crank. This gives an additional constraint that can be used in the resolution of the force redundancy by optimization techniques.

This paper discusses the compliance control of an ultrasonic motor powered prosthetic forearm which utilizes cutaneously measured electromyogram (EMG) signals sensed with the electrodes over the muscles as means of detecting motor commands sent by the central nervous system (CNS). Compliance control of the artificial limb was studied by implementing the bilinear model of the forearm and hand. This model emphasizes the role of the visco-elastic properties of the musculo-skeletal system of the actual limb in controlling its net configuration and movement. The flexor and extensor muscles extending over a joint influence the total joint impedance and determine the equilibrium position of the joint. Relaxing both flexor and extensor muscles makes the joint compliant to external forces, while activating both muscles increases the impedance of the joint.>

Studies multiple-robot manipulator cooperative manipulation of an unknown dynamic object. The authors show a simple approach for each robot to identify the object dynamics through cooperation. The authors decentralize the position/internal force hybrid controller such that each robot has its controller and determines its control torque from its own feedback information. Experiments show the effectiveness of the control strategy.>

This paper discusses compliant manipulation of multiple robot manipulator's cooperative in a dynamical environment. Cooperative compliant manipulation requires the simultaneous control of both the object's position and the interaction forces between the robots and the object. It is shown that position-controlled manipulators can not perform this kind of task successfully. It is proved that, in order to avoid excessive pressing, the frequency bandwidth of the overall control systems including the manipulators and the object should be adjusted according to the object dynamics. A unified model-matching control approach is developed in which each autonomous robot uses its own position and interaction force information to adjust the system's frequency bandwidth and to maintain the internal force in a desired performance. The reference models of the object's position and the internal force are selected with respect to the object's dynamics, and the force feedback controllers are designed accordingly to realize the reference models. Simulation studies show the effectiveness of this control approach.

The problem of designing the control for a robot that performs compliant manipulation on dynamic environments is studied. Compliant manipulation requires the controlled robot not only to follow the input trajectory exactly in free motion space, but also to manipulate adaptively on dynamic environments while making compliant contact with the environment's dynamic constrained space. An original model matching control approach for the control design is presented. The problems of selecting a reference model according to the environmental dynamics, its dynamic model uncertainty, the contact discontinuity and the robot actuator's output limitation are studied. The problem of how to implement such a reference model in a robot's control system is considered. In addition, adaptive algorithm for the robot manipulator are given to improve its performance when imposing compliant manipulation on a dynamic environment with parameter uncertainties. The effectiveness of this approach compared to the impedance control approach is illustrated through computer simulations and experiments.>

The problem of control design of robot performing compliant manipulation is addressed. Compliant manipulation fundamentally requires that the controlled robot not only follow up the input trajectory exactly in free motion space, but also manipulate adaptively on dynamic environments while making compliant contact with its dynamic constrained space. A model-matching approach for the control design of robot manipulators is presented. The problems of how to select the reference model according to environment dynamics (while considering its dynamic uncertainty, the contact discontinuity, and the robot actuator's output limitation) and how to implement such a reference model in a robot control system are analyzed. The effectiveness of this approach is illustrated by computer simulation and experimental results compared with impedance control approach.>