Pneumatic soft robots are well-suited for minimally invasive surgery owing to their compliance and safe interaction with tissues. However, achieving highly adaptive control is difficult owing to modeling inaccuracies, inter-chamber coupling, and disturbances from surgical instruments. Non-learning adaptive methods depend on simplified models and perform poorly in unstructured settings. Conversely, learning-based methods often impose high computational costs in multi-degree-of-freedom (multi-DoF) pneumatic systems. A previous study proposed a generative adversarial network (GAN)-based proportional–integral–derivative (G-PID) controller that combined PID stability with learning-based adaptability by aligning system behavior with a reference model. However, its performance in highly coupled multi-DoF pneumatic soft robots was unverified, and its online adversarial training was computationally intensive. We addressed these limitations by developing an offline-trained G-PID controller, shifting adversarial training offline to reduce computational overhead, achieving 23-fold faster convergence, and enabling real-time, model-free control with balanced adaptability and efficiency. We evaluated three multi-DoF data fusion strategies, showing effective coordination of DoF coupling while maintaining individual control fidelity. Validation on a multi-DoF soft robotic mechatronic system for single-port transvesical prostatectomy revealed tip errors below 0.16 mm across surgical instruments. Proposed controller enhances scalability and adaptability and may generalize to other mechatronic systems with nonlinear, coupled dynamics.

Recent advances in code generation and reasoning have enabled the growing use of large language models (LLMs) in robotics. The design of traditional robotic feedback controllers often requires extensive expert knowledge and iterative computational effort, motivating the exploration of LLM-assisted controller development. However, existing efforts are largely restricted to overly simplified systems, provide limited comparison with human-designed controllers, and lack validation on real-world robotic platforms. To address these gaps, this work investigates the use of LLMs in robotic feedback control by focusing on adaptive control and proposing an LLM-guided adaptive compensator. Instead of generating a complete controller from scratch, the LLM is guided to design a compensator that modulates the response of an unknown system to match that of a predefined reference system, thereby achieving adaptivity. The proposed approach is evaluated using five adaptive controllers on a two-degree-of-freedom (DoF) soft robot and a one-DoF shoulder joint of a humanoid robot. Both simulation and real-world experiments demonstrate that the LLM-guided adaptive compensator achieves performance comparable to four conventional adaptive controllers. Lyapunov-based analysis further establishes the stability and generalization capability of the proposed compensator, while analysis of the reasoning process suggests a more structured design approach. This study introduces a novel framework for integrating LLMs into robotic feedback control, with promising practical applicability.

The electrical property (EP) of human tissues is a quantitative biomarker that facilitates early diagnosis of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is an imaging modality that reconstructs EPs by the radio-frequency field in an MRI system. MREPT reconstructs EPs by solving analytic models numerically based on Maxwell's equations. Most MREPT methods suffer from artifacts caused by inaccuracy of the hypotheses behind the models, and/or numerical errors. These artifacts can be mitigated by adding coefficients to stabilize the models, however, the selection of such coefficient has been empirical, which limit its medical application. Alternatively, end-to-end Neural networks-based MREPT (NN-MREPT) learns to reconstruct the EPs from training samples, circumventing Maxwell's equations. However, due to its pattern-matching nature, it is difficult for NN-MREPT to produce accurate reconstructions for new samples. In this work, we proposed a physics-coupled NN for MREPT (PCNN-MREPT), in which an analytic model, cr-MREPT, works with diffusion and convection coefficients, learned by NNs from the difference between the reconstructed and ground-truth EPs to reduce artifacts. With two simulated datasets, three generalization experiments in which test samples deviate gradually from the training samples, and one noise-robustness experiment were conducted. The results show that the proposed PCNN-MREPT achieves higher accuracy than two representative analytic methods. Moreover, compared with an end-to-end NN-MREPT, the proposed method attained higher accuracy in two critical generalization tests. This is an important step to practical MREPT medical diagnoses.

Tactile perception on our fingers is a key sensory feedback that enables us to perceive and explore our world using our hands as probes, and is essential for efficient gripping and manipulation of objects. A tactile feedback system can therefore greatly improve the quality of life of individuals with partial or complete sensory loss like during stroke, or with artificial limbs after an amputation. However, most existing tactile texture feedback technologies suffer from two constraints. First, texture decoding and texture feedback have been traditionally examined separately and not as parts of the same problem, and second, texture information has been popularly fed back using sensory modality other than tactile itself. In this study, we propose a prototype on-line direct-texture decoding and feedback system in which the texture touched by a user is decoded using an accelerometer attached to the finger. The feedback is realized by rubbing the user’s skin with the actual material and the speed of the user swipes. The efficacy of the proposed system was tested in two user experiments with five test materials. The results and the corresponding hints for future improvements are discussed.

Because of the workforce shortage in Japan, the use of home-care robots, or carebots, is increasingly perceived as a realistic option. Developing and implementing these carebots requires careful consideration of the ethical implications for all types of users. Few studies, however, have addressed the ethical principles and concepts involved in carebot use, and consequently, the discussion regarding roboethics in the home-care environment has been inadequate. This questionnaire study explored the relationship between the willingness of home-care professionals to use carebots, their experiences with robots, and their ethical perceptions. The principal factors affecting home-care staff perceptions were perceived benefit, use of personal information, the protection of privacy, and perceptions of risk. While perceived benefit was the common predictor affecting home-care staff willingness to use a robot for the care of all user types, concerns regarding the use of personal information were more prominent for older people.

Studies on the Human Visual System (HVS) have demonstrated that human eyes are more attentive to spatial or spectral components with irregularities on the scene. This fact was modeled differently in many computational methods such as saliency residual (SR) approach, which tries to find the irregularity of frequency components by subtracting average filtered and original amplitude spectra. However, studies showed that the high frequency components have more effect on the HVS perception. In this paper, we propose a 2D mel-cepstrum based spectral residual saliency detection model (MCSR) to provide perceptually meaningful and more informative saliency map with less redundancy and without down-sampling as in saliency residual approach. Experimental results demonstrate that proposed MCSR model can yield promising results compared to the relevant state of the art models.

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.

NOTES (Natural Orifice Translumenal Endoscopic Surgery), a recently developed form of minimally invasive surgery, has attracted attention as a new laparoscopic operation. However, manipulation of forceps throughout the surgery continues to be a burden to surgeons. Our previous research focused on designing a model that could carry forceps and a camera through the abdominal cavity. The first prototype, a wire-tube driven micro robot, relied on the action of two suction cups for movement along the peritoneum (abdominal wall). However, the actions required to carry out the turning motions of the first prototype were strongly influenced by the rigidity of the tube. In order to solve this problem, in this study, we proposed a wire-wire driving mechanism for the robot, then developed a simulation model for investigating design and control issues. Based on the investigation, a new prototype was designed to determine the feasibility of the robot. Our results showed the robot's potential as a NOTES support device.

Recently, NOTES (Natural Orifice Translumenal Endoscopic Surgery) has attracted attention as a new approach in laparoscopic surgery. This approach achieves extremely less invasive surgery, however, manipulation of forceps is very difficult in NOTES, which brings more burdens on the surgeons. Under these situations, the aim of this study is to develop a micro mobile robot that could move in the abdominal cavity, loaded with a camera and/or forceps. In order to achieve stable movements and surgery support actions, we proposed using peritoneum (abdominal wall) as the surface for moving in the abdominal cavity. Moreover, we devised a robot system that relies on two suckers to attach to the peritoneum, and a cable driven mechanism to realize the relative movement of the two suckers, by which 3 D.O.F. (Degree Of Freedom) movements, i.e., moving forward/backward, turning left/right, and moving up/down could be realized. After examining the suction availability of suckers employed, we built a prototype robot system. We verified that, hanging upside down on the surface of vinyl wall, the robot could accomplish the designated movements with a certain load, which showed potential of using the robot system as the surgery support for NOTES.

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.

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%.