This paper introduces a novel cable-driven rodent ankle exoskeleton system designed for in-vivo research on the restoration and enhancement of sensorimotor abilities. The system features a lightweight, actuator-decoupled exoskeleton for shaping motion and providing kinesthetic feedback, along with a vision system and feedback-controlled treadmill for gait analysis. Experiments conducted under anesthesia and in awake conditions demonstrated effective control with minimal interference to natural gait. Dynamic time warping distance and Pearson correlation coefficients were calculated between joint angles from natural gait and those from rats wearing both passive and active exoskeleton component. The knee joint showed a low DTW distance and high correlation regardless of conditions, while all three joint displayed a greater maximum value from natural gait when the active component was engaged. These results provide valuable insights into the physiological impacts of wearable robotics in animal models, advancing sensorimotor rehabilitation technologies.

Robotic pushing is one of the intuitive nonprehensile manipulation skills that can handle ungraspable objects without any complex task-specific tools. In this paper, we proposed a goal-driven accurate robotic pushing framework to achieve the robotic pushing tasks in practice that can operate under uncertain object properties. We employed a model predictive path integral (MPPI) as a goal-driven pushing controller building upon our prior work to operate pushing tasks under uncertain object properties. Unlike our prior work, the proposed framework can push the object toward the goal pose without predefined trajectories. The results of the numerical experiments demonstrated that the proposed framework can accomplish the pushing task with a significantly shorter total length, smaller total step, and a higher success rate even though the model parameters are unknown. Moreover, we demonstrated the proposed framework also works well in the real world through real-robot demonstrations.

Haptic devices are widely used as control interfaces for robotic teleoperation, offering intuitive rendering of interactions between remote robot and environment. In particular, cutaneous feedback devices provide intrinsic stability and reduced form factor compared to kinesthetic feedback interfaces. However, the implementation of cutaneous feedback devices in industrial settings must be rigorously validated to prevent potential equipment accidents, which could lead to substantial economic losses due to unskilled robot manipulation. This paper presents a novel ungrounded haptic control interface (POstick-VF), designed specifically for high-risk steel production tasks. POstick-VF offers visuo-tactile feedback within an extensive workspace, enabling intuitive robot manipulation through its kinematic similarity with real tools ensuring safety. The performance of the developed POstick is rigorously validated and compared with conventional joystick controller through experiments conducted with an on-site hydraulic robot.

The implementation of digital twins (DTs) in industrial environments poses significant challenges, especially for tasks requiring direct physical interaction. This is critical in high-risk environments where system failures can lead to severe human and economic losses, and where even minor errors can have catastrophic consequences. Traditional robotic teleoperation systems often lack intuitive control and require extensive training, increasing the risk of malfunction and accident. This study addresses these challenges by introducing DT-based teleoperation for steel production, focusing on the hazardous lump iron removal process. We present two novel haptic interfaces: POstick-KF, offering enhanced kinesthetic feedback, and POstick-VF, providing a larger workspace with visuo-tactile feedback. These interfaces are designed to mimic real tools, ensuring intuitive control and quick operator training. A user study shows that POstick-VF significantly improves tracking accuracy for novices, while POstick-KF excels in interaction performance regardless of experience. In particular, POstick-VF improves interaction skills over time, and achieves comparable performance to POstick-KF, but with a simpler system. These interfaces have been rigorously tested to ensure robustness and are currently undergoing field testing for real-world applications. Our findings highlight the potential of these haptic devices to achieve the safety and efficiency of DT-based teleoperation within high-risk industrial environments, marking a significant advance in the field.

Steel mill is one of the most extreme and hazardous working environments due to molten iron erupted from blast furnace. Current manual labor to remove lump iron near the outlet, which is essential to prevent lump iron from scattering or blocking of molten iron, is performed by equipped human workers using a long stick tool. Thus, implementation of robotic teleoperation system is in demand to ensure the safety of workers. However, the conventional command interface is not intuitive for tool manipulation (i.e. pivoting, sweeping). Besides, haptic interface, which is used to render interaction results efficiently, still limits performance due to narrow workspace and insufficient kinesthetic feedback output compared to requirements. This paper proposes a novel haptic command interface (POstick) specified to lump iron removal task with two types (KF and VF). Both POsticks have rod-shaped end tip which is identical to actual tool already used to accelerate training. POstick-KF has large workspace and high kinesthetic feedback output satisfying requirements. Further, POstick-VF has strength with unlimited workspace at the expense of the amount of haptic information from simple vibrotactile feedback. User study to compare the performance of POsticks and conventional interface reveals that POstick-KF and VF showed superior interaction and tracking ability, respectively. Moreover, these two properties are in trade-off relationship that cannot be compatible. Finally, we proposed a seamless and automatic conversion mechanism from POstick-VF to KF, and vice versa, to cover up inherent limits of haptic devices.

This paper proposes a control structure for accurate tracking and compliant behavior of industrial manipulators without additional sensors. To achieve control objectives, friction, one of the biggest causes of performance degradation, should be compensated. For tracking performance, the estimated friction cancels most friction effects as a feed-forward, and the modified robust control structure eliminates the remaining friction uncertainty, which was originally equivalent to the disturbance observer. For compliant behavior, the compensation force fed to the real plant is bounded in contrast to the conventional disturbance observer structure. The compensation bound could be determined through the experiments. The proposed method is validated by experiments with a 6-DOF collaborative industrial manipulator.

This paper proposes a teleoperation framework to exploit the maximum manipulation capability during teleoperation. Here, exploiting maximum manipulation capacity means that the robot moves with its maximum control input while not violating the given constraints, and it is a nonlinear optimization problem with nonlinear constraints which is hard to be solved. The proposed framework relaxes the optimization problem into a simple QP problem and unifies the various constraints in the joint configuration space and Cartesian task space by utilizing control barrier function and control Lyapunov function techniques. The joint angle, velocity, acceleration limits are imposed on a teleoperated robot so that the robot does not generate any emergency stop during the teleoperation. Especially, the robot shows stable motion even near the kinematic singularity, so the operator can explore almost every reachable and admissible state of the robot via teleoperation.

This paper analyzes the effect of control param-eters of feed-forward and inner loop velocity controller in an admittance control scheme on the performance and passivity. The interaction force, inertia, and damping compensation were considered as the feed-forward input. Sufficient conditions and guidelines for each parameter were provided to enable the implementation of a wide range of desired admittance satis-fying passivity. The proposed guidelines were verified through experiments.

This paper proposes an admittance controller-based teleoperation system for contact-rich tasks. Based on the analysis of the motivating task (deposited iron lump removal task in the steel mill), the system concept is focused on the practical aspects of the system, and various components are combined to enhance the safety of the teleoperation of the robot. To connect the large inertia difference between the teleoperated robot and the command device, the admittance control is utilized in the teleoperation system, and the virtual spring saturation is adapted with the damping injection to ensure safe motion during the task. Lastly, the inertia and damping adaptation rule based on the contact force frequency is developed so that the system can selectively dissipate energy when the system shows oscillatory behavior. The proposed techniques have shown their effectiveness through the experiments. Although this study has started from a specific target, it suggests a practical solution for various contact-rich teleoperated tasks in the hazardous industrial workspace.

In corneal transplantation, 16 submillimeter-sized sutures are generated around the circular-shaped graft cornea. A major challenge in corneal transplantation is the generation of uniformly shaped sutures, where the suture shapes need to be changed depending on the patient. In this study, a corneal suturing robot that can produce sutures of the desired shape with high uniformity is described. The proposed robot manipulates the corneal tissue deformation before inserting the needle for suture shape control. A simulation using the finite element method was proposed to predict the suture shape. The suturing experiment was conducted on a porcine model, and the result was compared with the simulation. As a result, the proposed robot was able to generate sutures with a standard deviation of 108 μm in length and 36 μm in depth. The mean absolute error between the simulation and the experiment was 95 μm in length and 31 μm in depth. This is the first study to suggest a method for controlling the suture shape and quantitatively analyzing it in ophthalmic surgery.

Surface electromyography contains muscular information representing gestures and corresponding forces. However, conventional sEMG-based motion recognition methods, such as pattern classification and regression, have intrinsic limitations due to the complex characteristics of sEMG signals. In this paper, motion intensity, a highly selective sEMG feature proportional to the level of muscle contraction, is proposed. The motion intensity feature allows proportional and simultaneous recognition of multiple degrees of freedom. The proposed method was demonstrated in terms of simultaneous recognition of wrist/hand motions. The result shows that the proposed method can successfully decompose sEMG signals into highly selective signals to target motions. In future works, the proposed method will be adapted for more subjects and to sEMG applications for practical evaluation considering various grasping motions.

Robust particle manipulation is a challenging but essential technique for single-cell analysis and processing of microfluidic devices. This paper proposes a micro-particle manipulation system with a microfluidic channel network. We built gravity-induced pressure actuators, which can generate high-resolution output pressure with a wide range so that the multiple particles can be delivered from the inlet of the chip. In this paper, we studied how to model the proposed multi-input-single-output system and sources of disturbances, and designed a robust controller using disturbance observer technique. The performance of the proposed system was verified through experiments.

We develop a miniature suction-gripper with the goal to realize the novel robotic surgical instrument that can grip slippery and flexible peripheral nerves. In developing the instrument, we place a priority on devising the method that can robustly grip the nerve bundles during the surgical operation for the peripheral nerve. Also, we concentrate to investigate the working principle being able to minimize nerve damages that might be caused when manipulating the nerve. In this study, as the most suitable method to achieve the goal, we scheme to utilize the suction mechanism. Because it can non-invasively grip the nerve based on negative pressure, no external force is applied to the nervous tissues. Therefore the peripheral nerve can be manipulated without serious nerve damage (e.g. crush injury and stretch injury). To improve the gripping ability of the proposed suction gripper, two different types of microneedle arrays are applied to the suction-tips: passive-microneedle (PMN) arrays and active-microneedle (AMN) arrays. Since the most outer membrane of the nerve can be anchored by the penetrated PMN and AMN, the gripper can grip the nerve more robustly. The designed suction-gripper is fabricated as a functional prototype, and its working performances are assessed with in-vitro and in-vivo animal experiments. The experimental results well demonstrate the practical effectiveness of the proposed method and its applicability to the neurosurgical robot for the peripheral nerve.

We develop a novel cube-shaped finger-wearable haptic interface named as HaptiCube. With the aim of implementing the tactile device with improved functionality and wearability, we focus on designing the device to have two characteristics: multi-DOF force feedback displayability and large force capability with compact and lightweight structure. In designing the device, we mainly consider the type and configuration of actuator and driving mechanism, since both have a great effect on the size, weight, and output force of the device. As the actuator, we select the shape memory alloy exhibiting high energy density. And, as the driving mechanism, two different types of compliant mechanism are designed for two important functions: to convert contraction of the SMA to the desired motion as a motion guide and to apply bias-force to the SMA as a bias-spring. The device is designed as the miniature interface that can display 3-DOF pressure and 2-DOF shearing force to user's fingerpad. And, it is implemented as the functional prototype with the total weight of 26 g including actuators and all mechanical components. The experimental results on working performances (e.g. stroke, output force, and bandwidth) demonstrate that the device has superiority in terms of multi-DOF displayability, compact-sizability and wearability.

Impedance control allows robots to manipulate physical interactions delicately. However, issues associated with path planning and impedance remain unresolved. Herein we propose a path and impedance planning method for impedance control in a robot based on programming by demonstration through telemanipulation using a surface electromyogram. We considered a task that requires quick and precise adjustment of path and impedance, that is, ball trapping. We implemented a teleoperated robot that can deliver an operator's impedance as well as position during the ball trapping task to the slave side. The operators were asked to perform demonstrations of ball-trapping tasks using the implemented teleoperated robot, where the slave side is a vertical robot with one degree of freedom. The path and impedance were recorded and programmed as control input profiles from a set of successful demonstrations using Gaussian mixture regression. The result showed that using the human demonstration, the robot could learn how to catch a dropped ball without rebounding.

In this paper, we present KULEX-hand, a novel underactuated hand exoskeleton for grasping power assistance for patients having a partially paralyzed hand or the elderly with weakened muscle strength. This mechanism consists of an underactuated finger for grasp motion generation, a spherical four-bar linkage for power transmission, and a passive thumb link with a flexure hinge structure. Based on the natural closing motion of the human index finger, the motion generation linkage was synthesized as a planar five-bar in which two input links were coupled with a four-bar linkage. Therefore, after contact occurs on the proximal link, the synthesized linkage can mimic the grasping motion of the human middle and distal phalanges. The kinetoelastic relation of the underactuated finger was derived using the theory of screws. Based on this relation, guidelines were proposed for selecting the springs for achieving a stable pinch grasp. A prototype was designed, and the naturalness of motion was evaluated from an experiment with five subjects.

This paper presents preliminary results of a handheld device to assist the insertion of intra-fascicular planar electrodes into a peripheral nervous system. The developed device consists of two units, a nerve holder and an electrode inserter. We introduce design considerations, features, and underlying mechanisms of the device. User tests and animal experiments show that users can easily and accurately adjust the insertion position and direction of nerve electrodes while manipulating the device in 3D, and that planar electrodes are successfully inserted into sciatic nerves of rats. We hope that the proposed device will help neural engineering researchers and scientists to simplify the surgical process and produce consistent experimental results.

This paper presents a real-time framework for tele-manipulation by using sEMG signals to estimate both human motion and force intention. Our previous study showed that the ability to detect discrete force levels was not applicable to complex tasks such as grasping, holding, and manipulating various objects with variable force. Consequently, we identified the need to simultaneously track the arm and hand configurations and estimate the grasping force. However, it is difficult to continuously estimate the grasping force because of the time-varying nature of surface Electromyogram (sEMG) signals, even if a force remains constant. To solve such a problem, this study proposes a new regression strategy to enable continuous and proportional measurements and transmission of the grasping force by using sEMG signals in transient and steady-states. A 7-DOF robot arm with a robotic hand was able to remotely imitate a subject via an easily-wearable sEMG and inertia measurement units sensor interface. The experimental results verified that the motion and force capturing system successfully enabled interaction tasks, such as grasping, holding, and releasing motions with objects, with reliable and continuous force estimation.

This paper presents a control method of multi-DOF power assistant robots for anatomical multi-axis joints such as the wrist and the ankle. It is difficult to calculate the accurate direction of human motion intention during manipulating an object due to discrepancy between the calculated force from F/T sensor and the real human intention. Only using an sEMG is not an adequate method of power assistance for unknown external perturbation in the anatomical multi-axis joint, because the sEMG signal cannot figure out where the intention vector exists during interactions. This paper proposes a robust control method of power-assistant robots for unknown external perturbation during manipulating an object by using both the F/T sensor and sEMG. The specific purpose of this study to control the exoskeleton robot for the wrist motion during manipulating an object, although the accurate intention vector of the wrist joint is unknown. It was verified that the proposed method generates the assisted power to follow the human motion intention even in the case of unknown external forces through experiments.

In this paper, we present a real-time motion and force capturing system for tele-operated robotic manipulation that combines surface-electromyogram (sEMG) pattern recognition with an inertia measurement unit(IMU) for motion calculation. The purpose of this system is to deliver the human motion and intended force to a remote robotic manipulator and to realize multi-fingered activities-of-daily-living (ADL) tasks that require motion and force commands simultaneously and instantaneously. The proposed system combines two different sensors: (i) the IMU captures arm motion, (ii) and the sEMG detects the hand motion and force. We propose an algorithm to calculate the human arm motion using IMU sensors and a pattern recognition algorithm for a multi-grasp myoelectric control method that uses sEMG signals to determine the hand postures and grasping force information. In order to validate the proposed motion and force capturing system, we used the in-house developed robotic arm, K-Arm, which has seven degrees-of-freedom (three for shoulder, one for elbow, and three for wrist), and a sixteen degrees-of-freedom robotic hand. Transmission Control Protocol Internet Protocol (TCP/IP)-based network communication was implemented for total system integration. The experimental results verified the effectiveness of the proposed method, although some open problems encountered.

This paper proposes an integrated control method including learning with an ellipsoid function and impedance controller for rehabilitation using power-assisted robotic devices. The proposed controller consists of two parts of a primary algorithm, which are ellipsoid regression method for re-designing trajectory and impedance controller with pseudo mass/inertia. The ellipsoid regression method generates reference impedance profiles though acquiring motion and force trajectories during rehabilitation tasks assisted by therapists. The assisted force is controlled by impedance controller during execution of rehabilitation task using a concept of pseudo mass/inertia. The proposed method offers the power-assisted rehabilitation as guided by therapist, without consistent help from the therapist or other assisters. The proposed control method is validated by experiments throughout a 2-DOF rehabilitation robot, KULEX-2DOF(KIST Upper Limb Exoskeleton - 2DOF).

In this study, we evaluated a fast training algorithm to decode human hand configuration from sEMG signals on the forearms of five subjects. Eight skin surface electrodes were placed on the forearm of each subject to detect the sEMG signals corresponding to four different hand configurations and relax state. The preamplifier, which has 100 - 10000 times amplification gain and a 15 - 500 Hz bandpass filter, was designed to amplify the signals and eliminate noise. In order to enhance the performance of the classifier, feature extraction using class information was developed. The randomly assigned non-update learning method guarantees high speed classifier learning. The algorithm has been verified by experiments with five subjects.

Since bio-electric signals such as surface EMG are easily influenced by undesired artifacts and experimental environments including various electrical noises by peripheral devices, the amplifier is an issue of great importance. Although most commercial surface EMG amplifier systems provide high performance in acquiring electric bio-signals, they are not convenient for myoelectric control applications because they usually use wet-type electrodes that should be attached to the skin individually and there are also some limitations to possible modifications. In this study, we propose and develop a surface EMG interface that employs dry-type electrodes, a single supplied circuit for reduced weight, two voltage followers to improve input impedance, and a modified driven-right-leg circuit using a virtual ground circuit. By adapting a wearable band-type interface. The EMG electrodes can be reused while offering high performance corresponding to that of commercial products. The developed surface EMG system was successfully applied to decode human motion intentions of eight different configurations and a rest condition by using a fast training algorithms in a non-targeted manner.

This paper proposes a novel fast classification system consisting of feature extraction and classifier to decode human hand configurations from multi-channel surface electromyogram (sEMG) signals that allows real-time classification of human movement intention as well as prothesis control. In order to enhance the learning speed and the performance of the classifier, we used a supervised feature extraction method (called class-augmented principal component analysis) and a fast learning classifier (called extreme learning machine). Experimental results show that the proposed classification system quickly learns and decodes the human hand configuration with about 92% accuracy.

In bilateral teleoperation system, conventional position sensor based force reflection method, known as position error feedback, may generate inaccurate force reflection directions, when motion of a slave robot is constrained by unexpected obstacles and link collisions. Restriction Space Projection method is a novel position sensor based force reflection framework that was proposed to address this issue. It provides accurate force reflection in unstructured environments when motion of a slave robot is constrained by unexpected obstacles and link collisions, regardless of kinematic dissimilarity between the master and slave manipulators of the bilateral teleoperation system. This paper discusses the applications and limitations of the Restriction Space Projection method through examples.

This paper investigates a novel position-sensor-based force reflection framework for multi-degree-of-freedom (DOF) bilateral teleoperation systems in unstructured environments. The conventional position-sensor-based force reflection method, which is known as position error feedback, may generate grossly inaccurate force reflection directions during collisions involving the slave manipulator links. The proposed restriction space projection framework calculates the instantaneous restriction space to provide the accurate force reflection, regardless of kinematic dissimilarity (KDS) conditions of bilateral teleoperation systems. Simulation results confirmed the validity of the proposed framework in a KDS bilateral teleoperation system under various constraint conditions.

There are two main limitations in the conventional robot-assisted spinal fusion surgery. Since the end effector in the state of art has a role of guiding the insertion pose of a screw only, i) convenience that can be obtained when the robot intervenes in the surgery more actively could be limited, ii) The insertion pose of a screw provided by the robots could be deteriorated by surgeon's resisting force since he should insert a screw with his own hand withstanding the large reaction force transmitted through the drilling handle. To overcome those limitations, this paper proposes a novel approach for spinal fusion, wherein the robot performs the spinal fusion using the equipped end effector following surgeon's guide. We developed a dexterous small-sized the end effector that can perform previous gimleting and screwing tasks into the vertebrae. A five-DOF robot body that has kinematically-closed structure guides the insertion pose of a screw and resists strong reaction force firmly during the screwing process. Based on admittance control framework, the surgeon controls the pose of the end effector precisely to compensate induced static/dynamic errors during the operation. A torque feedback method without torque sensor that suggests the haptic information about the status of drilling is also included. The performance of the CoRASS was verified by experiments.

This paper presents a quantitative comparison framework for bilateral teleoperation systems which have different dynamic characteristics and sensory configurations for a given task dependant performance objective, mu-synthesis is used to develop the framework since it can efficiently treat systems containing uncertainties and disturbances. The framework consists of i) a feasibility test, and ii) a comparison methodology using prioritized task dependent performance objectives. This framework is applied to a bilateral teleoperation system including an uncertain human operator and environment in a practical case study. The validity of the proposed quantitative framework is confirmed through experiments. The proposed framework can be used as a tool to design bilateral teleoperation systems, especially when there are constraints in designing drive mechanisms and choosing sensory configurations.

In order to provide improved convenience for a surgeon in spinal fusion surgery, a robot system should i) closely engage in surgeon's operation using an end effector, and ii) protect the surgeon from being exposed to harmful radiation due to repeated shootings of fluoroscope. This paper proposes a bilateral teleoperation system for spinal fusion, BiTESS-II, to accomplish the goals. We developed an end effector that can substitute the surgeon's manual operation and a novel closed-loop type slave robot that can exert strong reaction force to complete gimleting and screwing tasks. Master devices are used to control the position and orientation of the slave robot and to generate haptic information identical to that of the slave side. A novel force reflection method without force sensors allowed to design the end effector simple and light. BiTESS-II is among the first human guided teleoperation system for spinal fusion with an adequate end effector. The performance of the BiTESS-II was verified by experiments.

This paper presents a quantitative comparison framework for bilateral teleoperation systems (BTSs) that have different dynamic characteristics and sensory configurations for a given task-dependent performance objective (TDPO).$\mu$-synthesis is used to develop the framework since it can efficiently treat systems containing uncertainties and disturbances. The framework consists of: 1) a feasibility test and 2) a comparison methodology using prioritized TDPOs. As the formulation used is based on$\mu$-synthesis, the system, operator, and environment models are represented in the form of linear nominal models with frequency-dependent multiplicative uncertainties. This framework is applied to a BTS including an uncertain human operator and environment in a practical case study. The validity of the proposed quantitative framework is confirmed through experiments. The proposed framework can be used as a tool to design BTSs, especially when there are constraints in designing drive mechanisms and choosing sensory configurations.

This paper proposes restriction space projection (RSP) method to generate accurate direction of force reflection when a bilateral teleoperation system (BTS) is kinematically dissimilar. Through examples, it is shown that the previous force reflection methods are not applicable to the kinematically dissimilar BTSs. Two kinds of RSP methods using a novel concept, instantaneous restriction space (IRS), are implemented for impedance and admittance type of BTSs. Especially, new developed obstacle avoidance algorithm using the redundancy of the slave manipulator makes the RSP method applicable to all kinds of kinematically dissimilar BTSs. Experiments verify that the RSP method is powerful to describe the restriction space at the slave side without force sensors

This paper proposes an accurate kinesthetic haptic display method for a multi-DOF haptic interface. Position-position (p-p) architecture plays an important role in a haptic interface like four channel architecture since force reflection using only force sensor information induces a serious problem if a slave manipulator is constrained by unexpected obstacles the force sensor cannot detect. However, the conventional p-p architecture in literature has limitations to apply to a multi-DOF haptic interface. This paper indicates the limitation through an example and proposes a novel haptic display method using instantaneous restriction space (IRS). IRS can be calculated using Jacobian and joint angle error of a slave manipulator. Since the proposed method has the form of impedance two port architecture in the sense of data flow, it can be easily combined with the previous results of two-port haptic display framework in literature. The method is compared to the conventional p-p architecture through experiments.

Since teleoperation systems are mostly executed in the extreme environment, there are constraints in designing the mechanism and choosing sensors. This paper presents a novel quantitative comparison method of teleoperators based on H/sub /spl infin// framework. The upper H/sub /spl infin// norm bound of the system including H/sub /spl infin// sub optimal controller is used as the performance index. As a case study, the method is applied to a real teleoperation system to study the effects of sensory configuration and back-drivability of the mechanism on the performance of the system in tasks, which involve different environment impedances. It can be important criteria to design a teleoperator from the control point of view.

Most tele-operation to manipulate an object consists of grasping and manipulation, and two or more 6-DOF haptic devices are usually used in master side. In this article, a new simply designed 7-DOF haptic device, PATHOS II is proposed for 1-DOF grasping and 6-DOF manipulation. The merits of a parallel type haptic device such as high stiffness and accuracy are natural characteristics of PATHOS-II with optimized workspace. Due to its unique symmetric structure, the isotropic manipulability is enhanced within the reachable workspace. This parallel type haptic device can be used in applications which need high precision, stiffness and isotropic manipulability.

Estimation of confidence intervals for neural network outputs is important when the uncertainty of a neural network system must be addressed for safety or reliability. This paper presents a new approach for estimating confidence intervals, which can help users validate neural network outputs. The estimation of confidence intervals, called error estimation by series association, is performed by a supplementary neural network trained to predict the error of the main neural network using input features and the output of the main network. The accuracy of this approach is shown using a simple nonlinear mapping and more complicated, realistic nuclear power plant fault diagnosis problems. The results demonstrate that the approach performs confidence estimation successfully.