The analysis of electromyographic (EMG) signals is crucial for advancing human-machine interaction. Despite recent progress, most methods still approach gesture intention prediction as a single classification task, which overlooks the complex temporal dynamics and channel-specific variations present in EMG signals. To address these shortcomings, we propose FESNet (Fine-grained Electromyography Segmentation Network), a novel segmentation-based network that temporally segments EMG signals, enabling a more fine-grained analysis of finger movements. Our approach utilizes a robust backbone network for feature extraction, followed by a functional head that adapts to different granularities or task objectives (classification or segmentation). We evaluate our method on the Ninapro DB8 dataset, where FESNet outperforms previous models, demonstrating its superior performance. The source code is publicly available at: https://github.com/Dianli97/FESNet

Efficient processing of multichannel biosignals has significant application values in the fields of healthcare and human-machine interaction. Although previous research has achieved high recognition performance with deep convolutional neural networks, several key challenges still remain: (1) Effective extraction of spatial and temporal features from the multichannel biosignals. (2) Appropriate trade-off between performance and complexity for improving applicability in real-life situations given that traditional machine learning and 2D-based CNN approaches often involve excessive preprocessing steps or model parameters; and (3) Generalization ability of neural networks to compensate for domain difference and to reduce overfitting during training process. To address challenges 1 and 2, we propose a 1D-based deep intra and inter channel (I2C) convolution neural network. The I2C convolutional block is introduced to replace the standard convolutional layer, further extending it to several state-of-the-art modules, with the intent of extracting more effective features from multichannel biosignals with fewer parameters. To address challenge 3, we integrate a branch model into the main model to perform dynamic label smoothing, enabling the model to learn domain difference and improve its generalization ability. Experiments were conducted on three public multichannel biosignals databases, namely ISRUC-S3, HEF and Ninapro-DB1. The results suggest that the proposed method exhibits significant competitive advantages in accuracy, complexity, and generalization ability.

Biological signals classification is essential for human machine interaction. Although previous research has achieved high classification performance, compensating for domain shift due to the intra and inter individual variations remains a challenge. In this paper, we propose a novel dynamic label smoothing strategy, named DLS, to address this issue. The proposed DLS constructs an auxiliary neural network to adjust the true label and to supervise the primary neural network. Experiments on the NinaPro DB1 dataset demonstrate that the proposed DLS outperforms current state-of-the-art methods. Furthermore, the proposed DLS has significant potential for practical applications as it can maintain or even improve the performance of the primary neural network on noisy data. The source code is publicly available at: https://github.com/peijii/DLS

With the increase in the number of small unmanned aircraft systems (sUAS) flights, the risk of collisions between sUAS and manned aircrafts such as helicopters flying at relatively low altitudes is also increasing. To improve safety at the low altitude airspace, we have been developing collision avoidance methods for sUAS. In our previous study, we developed a collision avoidance method that takes advantage of the performance of a multirotor sUAS in a assumed collision between a multirotor sUAS and a helicopter. However, for the sake of simplicity, this previous study did not consider the method of acquiring location information. Therefore, this study conducted collision avoidance simulations in a multilateration (MLAT) environment, which is assumed in helicopter surveillance, to examine how MLAT surveillance characteristics affect avoidance behavior.

In this study, we propose a design methodology for anthropomimetic robotic wrists and forearms. Conventional robotic wrists and forearms have few examples of human mimicry, and they have been simplified. The ligaments and tendons of the robotic forearm proposed in this study were replicated using chain-stitched wires and arranged in a manner similar to the human anatomy. Motion capture and goniometer measurements were used to measure the range of motion (ROM) of the wrist and forearm, driven by 16 servomotors. The results of the experiment were compared with the human ROM reported in previous studies, and it was found that the developed robotic forearm could achieve a ROM similar to that of humans. The use of this robotic forearm in functional verification experiments will enhance our understanding of the human structure. The integration of mechanical mechanisms with structures created through evolution is expected to improve the functionality of future robots.

The activities of muscles in the forearm have been widely investigated to develop human interfaces involving hand motions, especially in the fields of prosthetic hands and teleoperation. Although surface electromyography (sEMG) is considered as an effective biological signal from which hand motions can be recognized, the availability and quality of sEMG data can limit the usability and intuitiveness of human interfaces. This article introduces force myography (FMG) as a supplementary signal and proposes a layered sEMG–FMG hybrid sensor that can measure both sEMG and FMG at the same skin surface location. Meanwhile, a layer fusion convolution neural network (LFC) is designed to extract multiscale features from sEMG and FMG. To evaluate the effectiveness of the hybrid sEMG–FMG sensor and LFC, a 22-hand motion classification experiment was conducted on nine able-bodied subjects. The recognition results indicated a significantly improved classification accuracy (p < 0.001) of the hybrid sEMG–FMG modality with respect to single sEMG or FMG modality. The classification accuracies (CAs) of LFC were compared with conventional machine learning methods, including support vector machine, random forest classifier, xgboost, and k-nearest neighbor. Compared with the single-modality sEMG, the CAs of the dual-modality sEMG–FMG using conventional methods, and LFC were improved by 21.31% and 16.71%, respectively. These results suggest that the layered sEMG–FMG sensing approach can effectively enhance the performance of human interfaces, which offers great potential in the clinical applications of sophisticated prosthetic hands and teleoperation.

This article focuses on the vibration reducing and angle tracking problems of a flexible unmanned spacecraft system subject to input nonlinearity, asymmetric output constraint, and system parameter uncertainties. Using the backstepping technique, a boundary control scheme is designed to suppress the vibration and regulate the angle of the spacecraft. A modified asymmetric barrier Lyapunov function is utilized to ensure that the output constraint is never transgressed. Considering the system robustness, neural networks are used to handle the system parameter uncertainties and compensate for the effect of input nonlinearity. With the proposed adaptive neural network control law, the stability of the closed-loop system is proved based on the Lyapunov analysis, and numerical simulations are carried out to show the validity of the developed control scheme.

This paper proposes a coupled tendon-driven waist joint for humanoid robots. The waist joint was designed as a 3 degrees of freedom (DOF) structure to simulate the motion of a human waist. The power transmission was designed by adopting a 3-motor 3-DOF (3M3D) coupled tendon-driven mechanism, so that the torque on the joints was multiplied. We derived the torque transmission formula and the rotation angle formula of the 3M3D tendon-driven structures and designed the waist joint by adopting an appropriate structure according to their features. To evaluate the accuracy and load capacity of the waist joint, we performed a rotational accuracy experiment and a maximum torque experiment. The experiment results showed that the maximum error of joint rotation was below 1°, and the maximum torque of the pitch, roll, and yaw rotations were 87[Nm], 53[Nm], and 22.2[Nm], respectively.

In the growing elderly population globally, patients with severe movement disorders account for a large proportion. Moreover, the development of intelligent service equipment can better assist them in their daily. This paper proposes a new service robot control system. The brain-computer interface (BCI) based on Steady-State Visual Evoked Potentials (SSVEP) is used to acquire and process electroencephalogram(EEG) signals and output various control commands accordingly. Then, considering the visual fatigue of SSVEP-BCI, we added an object detection method based on Yolov3-tiny and saliency prediction to identify the patient’s selection intention intelligently. The results show that the subject can successfully complete the object delivery task with an average accuracy of 90.3%. The proposed control system can help the patients control a service robot in a more intelligent and friendly way to realize some daily tasks.

This article proposes coupled tendon-driven joint modules for anthropomorphic robots. Fully actuated 2-degree-of-freedom (DoF) and 3-DoF joint modules are classified and analyzed based on the motor-joint routing matrix that describes the tendon routing structure between the motors and the joints. Two-motor 2-DoF (2M2D) modules, which share the same form of motor-joint routing matrix, are classified into four types: externally actuated, internally coaxially actuated, internally separately actuated, and hybrid-actuated according to the location of the motors. Three-motor 3-DoF (3M3D) modules are classified into four forms based on the four possible forms of motor-joint routing matrix: fully routed motor-joint, 1-unrouted motor-joint, 2-unrouted motor-joint, and 3-unrouted motor-joint. The 2M2D and 3M3D coupled tendon-driven joint modules are analyzed and compared with respect to the relationship between the motor torque and the joint torque. A 7-DoF anthropomorphic robot arm was implemented with one 3M3D module for the shoulder joint and two 2M2D modules for the elbow and wrist joints, respectively, to demonstrate the utility of the proposed joint modules. The arm weighed 2.2 kg and was able to lift a 1.5-kg load with full outreach. The characteristics of the joint modules were evaluated in a current consumption experiment and a position accuracy experiment, and the performance of the robot arm was evaluated in a master-slave manipulation experiment involving dexterous movements.

This paper presents a gear-driven prosthetic hand designed for toddlers with transradial amputation. The hand design considers three main issues: weight, cost, and operability. The prosthetic hand and the cosmetic silicon glove are made based on the dimensions of a real hand. The simple, stable, and reliable gear-driven transmission helps to reduce the weight and the cost. A small actuator is embedded in the palm. During the grasp, the four fingers and the thumb flexes and extends as a unit to provide a wide range of holding area. The kinematics and static analysis in grasping was performed and the simulation results were compared with measured data. The motion performance and practical operability of the proposed hand was verified experimentally by a test system and a transradial subject.

This paper presents a lightweight (<;250 g) and low-cost (<;350 USD) biomimetic prosthetic hand with two actuators embedded in the palm. One of them is employed for flexion/extension of the five digits, and the other one is used for the adduction/abduction of thumb. Thus, the hand can achieve major grasping tasks that account for about 85% of activities in daily life. The unique transmission provides various advantages such as a compact structure, weight saving, and short driven distance. Furthermore, by using 3D printing technology, most parts of the prosthetic hand were made to be much lighter and have a humanlike appearance, compared with conventionally manufactured artificial hand. Finally, the performance and practical applicability of the proposed design was verified experimentally through both of a motion verification and an intuitive control test by a healthy subject and a transradial amputee.

A 2 motor 2 degrees-of-freedom (2M2D) coupled tendon driven joint module is proposed as a basic component for robot arms. Torque reallocation via tendon coupling can enhance the output torque of one single joint. According to the motor position, the joint module is classified into four types: the externally-actuated structure, the internally-coaxially-actuated structure, the internally-separately-actuated structure, and the hybrid-actuated structure. The four structures are analyzed and compared, and their implementation design examples are given. Experiments comparing the proposed joint module with directly-actuated traditional joint suggested that the 2M2D coupled tendon-driven joint module can obtain high control accuracy, and the torque reallocation via tendon coupling is effective to improve output torque. Additionally, an anthropomorphic robot arm with low weight and high payload was developed to show the utility of the proposed joint module.

Implantable brain machine interfaces (BMI) enable severely disabled people high-performance real-time robot control and communication, utilizing high-quality intracranial neural signals. Electrocorticograms (ECoG) are useful for implantable BMIs because of not only their zero time-lag property but their high spatiotemporal resolution with long term stability also. Fully implantable devices for ECoG recording offer long-term home-use with 24/7 supports. This will help not only patients with restoring motor and communication control but also help their caregivers with reducing burdens of caregiving day and night. Until now, we established ECoG-based robot control and communication. High gamma activity (80-150 Hz) was a good decoding feature for ECoG-based real time decoding and control. Independent component analyses effectively extract neural information with dimensional reduction and contribute to improving decoding accuracy. Also, we are developing a 128-channel fully-implantable BMI device (WHERBS) for long-term home-use with 24/7 supports. We completed GLP tests and non-clinical long-term implantation. The next step is a clinical trial to confirm safety and efficacy of the implantable BMI.

This work reports research and development of a lightweight neuroprosthesis, which can control the impaired motion by using voluntary biological signal. The total weight of the upper limb neuroprosthesis is 900 g, which is 40% lesser than the commercially available ones. For a trans-humeral amputee who had targeted muscle reinnervation (TMR) surgery, pattern classification of five motions was possible by using surface electromyogram (s-EMG) extracted from four dry electrodes.

Powered prosthetic hands are becoming increasingly functional through sensory feedback. However, when using electrical stimulation as sensory feedback for electromyographic (EMG) prosthetics, stimulation artifacts may cause EMG data noise. Electrical stimulation and EMG measurements are therefore performed using time-division methods in rehabilitation facilities. Under time-division methods, EMG levels cannot be acquired at the stimulation time. Highly functional prosthetic hands that can estimate grip force, however, use advanced signal processing and require detailed EMG information. EMG measuring cycle expansion may make grip force estimation unstable. We therefore developed a grip force estimation system using muscle stiffness and EMG as the estimation source signals. The estimation system consists of a muscle stiffness sensor, an EMG sensor and an estimation algorithm. We chose a tray holding task for the system evaluation. A weight is dropped on the tray and subjects are expected to control the tray's attitude. Grip force, EMG, and muscle stiffness are measured, and the measured and estimated grip forces are compared. The proposed algorithm estimates grip force with an error of just 18[N], which is 30% smaller than in EMG-only methods. The system response time is lower than human mechanical reaction time, validating the effectiveness of the proposed method.

For use as five-finger myoelectric hands in daily living, robotic hands must be 1) lightweight and human sized as well as possess 2) human-like multiple degrees of freedom (DoF) and a high grip force. The compatibility of these requirements is a trade-off; ideal robotic hands have yet to sufficiently satisfy both these requirements. Herein a power allocation mechanism is proposed to improve the grip force without increasing the size or weight of robotic hands using redundant DoF during pinching motions. Additionally, this mechanism is applied to an actual five-finger myoelectric hand, which can make seven types of motions that are necessary for activities of daily living (ADL) and realizes about a 60% improvement in fingertip force, allowing three fingers to pinch objects exceeding 1 kg.

To classify the five individual finger motions from an electromyogram (EMG) signal, a classification system that hybridizes EMG signals in both the transient and converged states of a motion is proposed. The classifications of finger motions are executed individually in each state by a well-established artificial neural network (ANN). Then, the outputs of the two classifiers are combined. The efficacy of the result is evaluated via a piano-tapping task, in which the subjects are instructed to tap a keyboard with each of their five fingers. We use this task to compare the proposed hybrid system and a conventional converged system that uses an EMG signal only in the converged state. For five of the six subjects, the accuracy ratio of finger motions was better in the proposed method: approximately 85% for each finger except the second. Further analysis suggests two remarkable advantages of the hybrid method: (1) the output of the ANN is more credible, and (2) finger motion in the transient state (i.e., the early phase) is more predictable.

Long-time exposure to repetitive or monotonous work is associated with increased risk for musculoskeletal disorders that are due to muscle fatigue. Previously, researchers reported that muscle fatigue can be estimated using a low-frequency band of an SEMG signal. However, these studies were performed exclusively during static contraction tasks. The objective of the present study was to evaluate and determine the frequency range for a low-frequency band. In addition, the performance during dynamic contraction tasks was analyzed. A group of healthy university students (8 males) was recruited, and endurance handgrip tasks were conducted. SEMG signals were captured from the forearm muscle. The frequency range for the low-frequency band was redefined as 5 - 40 Hz. The results from a dynamic contraction task indicated that a low-frequency band is a reliable method for indexing muscle fatigue from SEMG signals.

We propose a load sensitive Continuously Variable Transmission (CVT) for a wire-driven robot hand ¿Drum CVT¿, and aims at achieving efficient finger motions by mechanically changing the reduction rate of the drive: fast finger motion with low load (i.e., low drive at fast motion) and slow finger motion with high load (i.e., high drive at slow motion). We developed two material types of Drum CVT: the deflection-type (using nylon) and the torsion-type (using metal). Both types are investigated in both theoretical and actual models, and demonstrated their performance. Eventually, we revealed those characteristics, and indicated the usage of each Drum-CVT.

We propose a novel robotic developmental kit for educational purpose. It helps junior high / high school / university students to understand recent prosthetic technology and, moreover, to provide a chance to produce creative prosthetic applications for short time at low cost. The developmental kit consists of an EMG-to-Motor controller and a wire-driven device. For delivering the cheapness and easiness to students, we demonstrate three prosthetic applications based on the kit: (1) Simple Prosthetic Hand is a mimic of commercial prosthetic hand. It illustrates that low-precise design achieves cheap production cost and sufficient function as a prosthetic hand. (2) Rock-Scissors-Paper Prosthetic Hand is based on prosthetic hands for research use. It clearly illustrates EMG-to-Motion discrimination processes by displaying real signals. (3) EMG Presbyopia Spectacles shows the possibility that beginners design creative prosthetic applications based on daily activities. Finally, we report two educational courses we have conducted for junior high and high school students.

Nowadays, the Man-Machine interfaces are becoming more important in our interaction with robotic systems. One particular case is on prosthetic devices, where the need of "biofeedback" is of vital importance to achieve subconscious control of these devices. In order to provide this feedback, so the device can be included into the user's body image, we use electrical stimulation as a substitute for tactile feedback. In this study we analyze the effects of the electrical stimulation together with the intention, provided by the electromyography, for the sensation generation, and its inclusion on the user's body schema. To evaluate our system, we use functional Magnetic Resonance Imaging, to measure the changes in the brain due to the use of an EMG controlled prosthetic hand with electrical stimulation as tactile feedback.

This paper describes a new real-time learning method for the development of a robust motion discriminating method from an EMG signal, to adjust to the change in user's characteristics. This method is done under the assumptions that the input motions are continuous, and the teaching motions are ambiguous in nature, therefore, automatic addition, elimination and selection of learning data are possible. Applying our proposed method, we conducted experiments to discriminate eight forearm motions, with the results, a stable and highly effective discrimination rate was achieved and maintained even when changes occurred in user's characteristics

We developed a new adaptable EMG prosthetic hand, which executes recognition process and learning process in parallel and can keep up with change in the mapping between an electromyographic signals (EMG) to the desired motion, for amputee. EMG-to-motion classifier which used in proposed prosthetic hand is done under the assumptions that the input motions are continuous, and the teaching motions are ambiguous in nature, therefore, automatic addition, elimination and selection of learning data are possible. Using our proposed prosthetic hand system, we conducted experiments to discriminate eight forearm motions, with the results, a stable and highly effective discrimination rate was achieved and maintained even when changes occurred in the mapping. Moreover, we analyzed mutual adaptation between human and adaptable prosthetic hand using ability test and f-MRI, and clarified each adaptation process

The purpose of our research is to build a system for mutual adaptation between a user and assistive devices for restoration of motor function. To build such system, it is necessary to know human's motion patterns. In this paper, as the first step, we investigated human motion characteristic on human-machine system like EMG (electromyogram) prosthetic hand and EMG to motion classifier system. In the experiment, we measured the EMG signals and investigated a difference between motion patterns of teaching motion, i.e. user's intended motion, and that of actual motion using the proposed criteria. As results, it is clear that these criteria are useful to analyze changes of human motion patterns

In this paper, we introduce our robot package for educational use. The main characteristics are the followings: robots are built by connecting plastic bottles and RC servo motors with glues so that technical skills such as machining are not required for students; three types of robot controllers, such as manual controller, autonomous controller, bio-signal interface controller, are provided so that students can experience autonomous robots and bio-signal interface techniques. Thus, this package provides opportunities to design both robot structure and control architecture and, moreover, to experience new engineering technologies. So far, we have conducted robot education courses for undergraduates and graduates three times. The first course purposed to teach students morpho-functionality, which is a concept of embodied artificial intelligence. As results, all the students have designed locomotive robots and understood "morpho-functionality." In the second and third courses, students have experienced to control locomotive robots with bio-signal interface techniques. Thus, we have shown that this educational package provide variety of robot techniques and, depending on course hour and students target, we can modify course programs

The purpose of our research is to build a system for mutual adaptation between a user and assistive devices for restoration of motor function. To build such system, it is necessary to know human's motion patterns. In this paper, as the first step, we investigated human motion characteristic on human-machine system like EMG (electromyogram) prosthetic hand and EMG to motion classifier system. In the experiment, we measured the EMG signals and investigated a difference between motion patterns of teaching motion, i.e. user's intended motion, and that of actual motion using the proposed criteria. As results, it is clear that these criteria are useful to analyze changes of human motion patterns.

Biped walking is one of the most graceful movements observed in humans. Today’s humanoid robots, despite their undeniably impressive performance, are still a long way from the elegance and grace found in Nature. To narrow the gap between natural and artificial systems, we propose to rely more on morphology, intrinsic dynamics, and less on raw computation. This paper documents a series of simulated and real “pseudo-passive” dynamic biped walkers in which computation is traded off for good morphology, that is, adequate mechanical design and appropriate material properties These two factors are parameterized, and the resulting solution space is explored in simulation. Interesting solutions are then realized in the real world. Our experiments show that successful pseudo-passive walkers with a good morphology locomote by converting oscillatory energy into forward movement.

SMA has been used as an actuator alongside with certain heat sinking mechanism which makes the structure of the actuator as a whole, bulky and heavy in nature. This paper describes the effort taken in speeding up the rate of heat transfer of the SMA in ambient environment by introducing a simple, new heat sink, consisting of a combination of an outer metal tube and silicone grease as cooling medium. The SMA wire of diameter 0.3 mm is first coated with a layer of silicone grease, and is inserted into the outer metal tube making sure that the silicone coated SMA does not inhibit the mobility of the outer metal tube. An experimental setting, with the task of using the SMA to vertically haul up a 3kg weight was set up. PWM (pulse width modulation) control was applied to the SMA and by using a position sensor, the rise and fall of weight can be easily monitored, therefore the response speed of the actuator can be observed. Meanwhile a temperature sensor, a thermocouple type-K, was used to monitor the temperature of the SMA for temperature control. Simulation results of the rate of heat transfer based on the heat transfer equation are also presented to validate the effectiveness of this proposed heat sink.

A power assisting device for lower back flexion and extension, when carrying a heavy load is presented in this paper. When people lift up something heavy, they use both hands. So the development of a controller without using hands is necessary. We attempt to control the device by a voluntary human motion. First, lifting up and putting down motions are analyzed using a human model. Results show that torques in a hip joint and a knee joint are characteristic for control the device. It means that EMG signals in front and back thigh muscles can become feature values because the muscles located there connect the hip joint and the knee joint. After that, a controller of the device using the EMG signals is developed. Finally, artificial neural networks (ANN) are introduced as a solution of the problem of individual differences.

We discuss the experimental implementation of a chemical controller for a robotic hand. In the present case study, we design a closed system in which a Belousov-Zhabotinsky (BZ) thin-layer chemical reactor linked to a robotic hand via an array of photo-sensors and fingers of the hand stimulates the excitation dynamics in a BZ medium. The principal working circle of chemo-robotic systems is that oxidation wave fronts traveling in the medium are detected by photo-sensors and cause (via a microcontroller) fingers to bend. When a finger bends, it applies a small quantity of colloid silver to the reaction and, thus, evokes an excitation wave. The traveling and interacting waves stimulate further movements of the fingers. In this paper, we offer an experimental set-up, including algorithms and interfacing, for an experimental chemical robotic hand controller, which contributes to the fields of non-classical computation, non-linear physics, and unconventional robotics.

The goal of this paper is to explore whisker systems as a sensory modality in animals (rodents) and robots with respect to their adaptive potential. To this end, we have developed an active whisker system. Whiskers from real mice, when attached to capacitor microphones, can detect very weak stimuli at their tips. Different eigen frequencies are induced in the whisker according to the materials over which the whiskers move. Whisking is active, i.e., they can move back and forth to induce sensory stimulation even when an agent is not moving. In this study, active whisking is applied to navigation tasks. The results show that materials can be distinguished using 2-3 kHz signals, which correspond to the experimental evidence obtained from animals.

Whiskers are powerful sensors for robots that are not only useful for basic tasks such as obstacle avoidance, but also have the potential for gathering rich information about objects. We have developed an active multi-whisker array modeled on the rat whisker system which can be amounted on a mobile robot. We show that with this whisker array we can discriminate different textures based on the frequencies elicited by the whiskers. We exploit the phase-locked structure of our data using sensory-motor integration. Two factors enable better discrimination of the textures: firstly, considering several touch events from one whisker; and secondly, combining the information from more than one whisker.

In this paper, we propose a power assist device for lower back flexion and extension, when carrying a heavy load. To see the effect of the device, we model a human body and analyze a compression force in his lower back, as well as the evaluation of a supported force at a hand position. A prototype of the device is manufactured and a controller of the device is developed, which can follow a voluntary human motion assisting his strength.

In this paper, we present a first series of experiments with prototype artificial whiskers that have been developed in our laboratory. These experiments have been inspired by neuroscience research on real rats. In spite of the enormous potential of whiskers, they have to date not been systematically investigated and exploited by roboticists. Although the transduction mechanism is simple and straightforward, and the whiskers are currently used in a passive way only, the dynamics of the sensory signals resulting from the interaction with various textured surfaces is complex and has a rich information content. The experiments provide the foundation for future work including active sensing, whisker arrays, and cross-modal integration.

Reconfigurable robots have functional flexibility and physical flexibility. A tripod robot is one of the robots having SMA actuators and it is characterized by its symmetrical structure and reconfigurability. When realizing movements of autonomous robots, the model of both the robot and the environment am required for the controller. In this paper, the model for control the tripod robot is generated as a logic circuit by Net-list evolution. The Net-list chromosome is one of the circuit models that abstracted a logic circuit structurally. The generation of the adaptive behavior of the tripod robot and the control model are discussed through the experiment of using a simulator.

In observing behavior of multiple autonomous robots, a microscopic observation expressed by dynamic equations is usually used. However, it is very difficult to estimate behavior of robots or mutual interactions among them in real time. Furthermore, it is hard to realize the observed system by taking consideration in all the factors of the system. On the other hand, a macroscopic observation defined by state equations is efficient for recognizing behavior of multiple robots. In this study, a quantitative observation approach is proposed on behavior of multiple robots. This approach introduces macroscopic state quantities in thermodynamics into expression of the multiple robots system. The advantage of this approach is that observation on the behavior of autonomous robots in real world can be mapped to characteristic quantities in another conceptual state space. At first, the state quantities of multiple robots system are defined and their physical meaning is discussed regarding it as quantitative observation of multiple robots. The macroscopic state quantities in thermodynamics, such as temperature, pressure and entropy, are introduced into mobile robots system. Each mobile robot is regarded as a particle in term of thermodynamic systems. Experiments show that the states of robots system can be classified from a viewpoint of the macroscopic state quantities in thermodynamics. This verifies that the macroscopic quantitative observation is efficient and applicable to controlling multiple robots system.

In order to achieve an autonomous system which can adaptively behave through learning in the real world, we have constructed a distributed autonomous swimming robot that consists of mechanically linked multi-agent and adopts adaptive oscillator method that was developed as a general decision making for distributed autonomous systems. One of the aims of using this system is to verify whether the robot could complete a target approaching including obstacle avoidance. For this purpose, we introduce a modified Q-learning in which plural Q-tables are used alternately according to dead-lock situations. By using this system, as a result, the robot acquires a stable target approaching and obstacle avoiding behavior.

The purpose of the article is the development of an interface which closely adapts to the individual. By quantifying the frustration as the human manipulates machines from a biomedical signal and making it into teaching signals of machine learning (ML), we aim at the development of a system in which the machine adapts to the human. The authors extract the characteristic vector of whether the examinee is in discomfort or not from electroencephalograms (EEG) and electromyograms (EMG). An artificial neural network (ANN) is employed to extract the vector. For the machine learning, reinforcement learning is used and the rewards are an extracted signal from physiological signals. As a basic experiment for extracting discomfort and comfort from the physiological signals, the EEG measurement experiment was carried out under an unpleasant sound environment for 20 examinees. The input signals to ANN for the characteristic vector extraction was examined. By affixing piezoelectric films on the eyebrow, the movement of the eyebrow was measured. Finally, the results of measuring EEG and EMG simultaneously under the situation in which frustration accumulated for the examinee are shown. We use reinforcement learning (RL) to control the behavior of the Khepera robot.

Proposes the construction of a "strong" autonomous mobile robot, which can acquire environment oriented behavior through learning, as a distributed autonomous system. It is thought that such a system has many advantages over other systems in terms of adaptability to the environment and so on. However, the potential of this type of system has yet to be demonstrated in experiments under real-world conditions. We conducted an experiment to determine whether a distributed autonomous swimming robot could acquire target-approaching behavior on a water surface which was set as the robot's work space. As a result, from a fairly simple coding, the robot acquired the reproducible target-approaching behavior using only local learning even in cases where a partial fault occurred, and the acquired actions also enabled the robot to approach the target in an environment with a narrow gate.

We discuss the necessity of a learning mechanism for an EMG prosthetic hand controller, and the real-time learning method is proposed and designed. This method divides the controller into three units. The analysis unit extracts useful informations for discriminating motions from the EMG. The adaptation unit learns the relation between EMG and control command and adapts operator's characteristics. The trainer unit makes the adaptation unit learn in real-time. Experiments show that the proposed controller discriminates ten forearm motions, which contain four wrist motions and six hand motions, and learns within 4/spl sim/25 minutes. The average of the discriminating rate is 91.5%.

This study presents an autonomous agent which is able to behave appropriately in the complex environment with unsophisticated sensors. To satisfy this aim, an internal cognitive space, called a metamorphic internal image space, is introduced. The metamorphic internal image space is a space that integrates the sensory inputs and the temporal change of the space, so that the agent is able to behave by relying only on the image. An agent with the metamorphic internal image is considered to have following two major characteristics: 1) it can learn the chain of actions considered as a behavior; and 2) it can move with/without the change of sensory input pattern. A learning mechanism based on a multi-agent system is introduced for the realization of the metamorphic internal image. The behavior of the multi-agent system can be regarded as a complex system. Computational experiments show the emergent behavior of the mobile agent with metamorphic internal image based on the complex learning system.

This paper proposes potential field techniques for control of a link mechanism based on a decentralized management and an approach to describing a mathematical model of autonomous machines. To control the group of machines, there are two categories of method, one was based on central control and the other on parallel distributed control. A popular method for control machines is the so-called potential method, which has been applied to navigation for automatically guided vehicles in a stable working space. Since the potential function of the working space must be determined at every instant for an unstable working space, an efficiency of the potential method becomes low. To modify the potential function local, an approach called the vibrating potential method (VPM) is introduced. With this method, there are many cases where high-performance information processing can be attained without supervision. This paper proposes a new control method based on VPM and discusses its application to the grasping problem of free form objects.