Human motor control remains agile and robust despite limited sensory information for feedback, a property attributed to the body's ability to perform morphological computation through muscle coordination with variable impedance. However, it remains unclear how such low-level mechanical computation reduces the control requirements of the high-level controller. In this study, we implement a hierarchical controller consisting of a high-level neural network trained by reinforcement learning and a low-level variable-impedance muscle coor dination model with mono- and biarticular muscles in monoped locomotion task. We systematically restrict the high-level controller by varying the control frequency and by introducing biologically inspired observation conditions: delayed, partial, and substituted observation. Under these conditions, we evaluate how the low-level variable-impedance muscle coordination contributes to learning process of high-level neural network. The results show that variable-impedance muscle coordination enables stable locomotion even under slow-rate control frequency and limited observation conditions. These findings demonstrate that the morphological computation of muscle coordination effectively offloads high-frequency feedback of the high-level controller and provide a design principle for the controller in motor control.

Abstract--In human-robot systems, ensuring safety during force control in the presence of both internal and external disturbances is crucial. As a typical loosely coupled floating-base robot system, the supernumerary robotic leg (SRL) system is particularly susceptible to strong internal disturbances. T o address the challenge posed by floating base, we investigated the dynamics model of the loosely coupled SRL and designed a hybrid position/force impedance controller to fit dynamic torque input. An efficient variable impedance control (VIC) method is developed to enhance human-robot interaction, particularly in scenarios involving external force disturbances. By dynamically adjusting impedance parameters, VIC improves the dynamic switching between rigidity and flexibility, so that it can adapt to unknown environmental disturbances in different states. An efficient real-time stability guaranteed impedance parameters generating network is specifically designed for the proposed SRL, to achieve shock mitigation and high rigidity supporting. Simulations and experiments validate the system's effectiveness, demonstrating its ability to maintain smooth signal transitions in flexible states while providing strong support forces in rigid states. This approach provides a practical solution for accommodating individual gait variations in interaction, and significantly advances the safety and adaptability of human-robot systems.

Despite a slow neuromuscular system, humans easily outperform modern robot technology, especially in physical contact tasks. How is this possible? Biological evidence indicates that motor control of biological systems is achieved by a modular organization of motor primitives, which are fundamental building blocks of motor behavior. Inspired by neuro-motor control research, the idea of using simpler building blocks has been successfully used in robotics. Nevertheless, a comprehensive formulation of modularity for robot control remains to be established. In this paper, we introduce a modular framework for robot control using motor primitives. We present two essential requirements to achieve modular robot control: independence of modules and closure of stability. We describe key control modules and demonstrate that a wide range of complex robotic behaviors can be generated from this small set of modules and their combinations. The presented modular control framework demonstrates several beneficial properties for robot control, including task-space control without solving Inverse Kinematics, addressing the problems of kinematic singularity and kinematic redundancy, and preserving passivity for contact and physical interactions. Further advantages include exploiting kinematic singularity to maintain high external load with low torque compensation, as well as controlling the robot beyond its end-effector, extending even to external objects. Both simulation and actual robot experiments are presented to validate the effectiveness of our modular framework. We conclude that modularity may be an effective constructive framework for achieving robotic behaviors comparable to human-level performance.

Reconfigurable intelligent surface (RIS) is a two-dimensional periodic structure integrated with a large number of reflective elements, which can manipulate electromagnetic waves in a digital way, offering great potentials for wireless communication and radar detection applications. However, conventional RIS designs highly rely on extensive full-wave EM simulations that are extremely time-consuming. To address this challenge, we propose a machine-learning-assisted approach for efficient RIS design. An accurate and fast model to predict the reflection coefficient of RIS element is developed by combining a multi-layer perceptron neural network (MLP) and a dual-port network, which can significantly reduce tedious EM simulations in the network training. A RIS has been practically designed based on the proposed method. To verify the proposed method, the RIS has also been fabricated and measured. The experimental results are in good agreement with the simulation results, which validates the efficacy of the proposed method in RIS design.

The technology for generating robot actions has significantly contributed to the automation and efficiency of tasks. However, the ability to adapt to objects of different shapes and hardness remains a challenge for general industrial robots. Motion reproduction systems (MRS) replicate previously acquired actions using position and force control, but generating actions for significantly different environments is difficult. Furthermore, methods based on machine learning require the acquisition of a large amount of motion data. This paper proposes a new method that matches the impedance of two pre-recorded action data with the current environmental impedance to generate highly adaptable actions. This method recalculates the command values for position and force based on the current impedance to improve reproducibility in different environments. Experiments conducted under conditions of extreme action impedance, such as position control and force control, confirmed the superiority of the proposed method over MRS. The advantages of this method include using only two sets of motion data, significantly reducing the burden of data acquisition compared to machine learning-based methods, and eliminating concerns about stability by using existing stable control systems. This study contributes to improving robots' environmental adaptability while simplifying the action generation method.

Intuitively, prostheses with user-controllable stiffness could mimic the intrinsic behavior of the human musculoskeletal system, promoting safe and natural interactions and task adaptability in real-world scenarios. However, prosthetic design often disregards compliance because of the additional complexity, weight, and needed control channels. This paper focuses on designing a Variable Stiffness Actuator (VSA) with weight, size, and performance compatible with prosthetic applications, addressing its implementation for the elbow joint. While a direct biomimetic approach suggests adopting an Agonist-Antagonist (AA) layout to replicate the biceps and triceps brachii with elastic actuation, this solution is not optimal to accommodate the varied morphologies of residual limbs. Instead, we employed the AA layout to craft an elbow prosthesis fully contained in the user's forearm, catering to individuals with distal transhumeral amputations. Additionally, we introduce a variant of this design where the two motors are split in the upper arm and forearm to distribute mass and volume more evenly along the bionic limb, enhancing comfort for patients with more proximal amputation levels. We characterize and validate our approach, demonstrating that both architectures meet the target requirements for an elbow prosthesis. The system attains the desired 120{\deg} range of motion, achieves the target stiffness range of [2, 60] Nm/rad, and can actively lift up to 3 kg. Our novel design reduces weight by up to 50% compared to existing VSAs for elbow prostheses while achieving performance comparable to the state of the art. Case studies suggest that passive and variable compliance could enable robust and safe interactions and task adaptability in the real world.

To manipulate objects or dance together, humans and robots exchange energy and haptic information. While the exchange of energy in human-robot interaction has been extensively investigated, the underlying exchange of haptic information is not well understood. Here, we develop a computational model of the mechanical and sensory interactions between agents that can tune their viscoelasticity while considering their sensory and motor noise. The resulting stochastic-optimal-information-and-effort (SOIE) controller predicts how the exchange of haptic information and the performance can be improved by adjusting viscoelasticity. This controller was first implemented on a robot-robot experiment with a tracking task which showed its superior performance when compared to either stiff or compliant control. Importantly, the optimal controller also predicts how connected humans alter their muscle activation to improve haptic communication, with differentiated viscoelasticity adjustment to their own sensing noise and haptic perturbations. A human-robot experiment then illustrated the applicability of this optimal control strategy for robots, yielding improved tracking performance and effective haptic communication as the robot adjusted its viscoelasticity according to its own and the user's noise characteristics. The proposed SOIE controller may thus be used to improve haptic communication and collaboration of humans and robots.

--Recent work introduced the concept of human teleoperation (HT), where the remote robot typically considered in conventional bilateral teleoperation is replaced by a novice person wearing a mixed reality head mounted display and tracking the motion of a virtual tool controlled by an expert. HT has advantages in cost, complexity, and patient acceptance for telemedicine in low-resource communities or remote locations. However, the stability, transparency, and performance of bilateral HT are unexplored. In this paper, we therefore develop a mathematical model and simulation of the HT system using test data. We then analyze various control architectures with this model and implement them with the HT system to find the achievable performance, investigate stability, and determine the most promising teleoperation scheme in the presence of time delays. We show that instability in HT, while not destructive or dangerous, makes the system impossible to use. However, stable and transparent teleoperation are possible with small time delays ( < 200 ms) through 3-channel teleoperation, or with large time delays through model-mediated teleoperation with local pose and force feedback for the novice. Many remote and underresourced communities experience severe challenges in accessing qualified medical care. For example, ultrasound imaging is important, widely used, and much lower cost than other modalities such as CT or MR. However, capturing and interpreting ultrasound images requires a high degree of expertise that is not commonly present in many small communities. As a result, a sonographer or radiologist must be transported to the town on a regular basis, or patients must be sent to a major medical center. Either case leads to long wait times and difficulty handling urgent cases. In communities across Canada, patients are flown hundreds of kilometers for standard ultrasound exams. This takes up to three days and exerts a high social and financial cost on the community. Therefore, tele-ultrasound is an important and growing field. However, current commercially available technologies are often impractical. Video teleguidance is simple, low-cost, and accessible to anyone but is highly inefficient and imprecise if the person being guided does not already have ultrasound experience [1]. On the other hand, robotic teleultrasound gives the physician complete and precise control but is expensive and complex to set up and maintain. We thus recently introduced a novel teleguidance method called human teleoperation to address the shortcomings of both existing approaches [1], [2]. This method is also applicable to many other remote guidance applications. In human teleoperation, a local novice, the "follower", performs an ultrasound exam on a patient while being guided by a remote operator, the sonographer or radiologist. The follower wears a mixed reality (MR) head-mounted display (HMD) which projects a virtual ultrasound probe into their field of view.

Lower Limb Exoskeletons (LLEs) are wearable robots that provide mechanical power to the user. Human-exoskeleton (HE) connections must preserve the user's natural behavior during the interaction, avoiding undesired forces. Therefore, numerous works focus on their minimization. Given the inherent complications of repeatedly prototyping and experimentally testing a device, modeling the exoskeleton and its physical interaction with the user emerges as a valuable approach for assessing the design effects. This paper proposes a novel method to compare different exoskeleton configurations with a flexible simulation tool. This approach contemplates simulating the dynamics of the device, including its interaction with the wearer, to evaluate multiple connection mechanism designs along with the kinematics and actuation of the LLE. This evaluation is based on the minimization of the interaction wrenches through an optimization process that includes the impedance parameters at the interfaces as optimization variables and the similarity of the LLE's joint variables trajectories with the motion of the wearer's articulations. Exploratory tests are conducted using the Wearable Walker LLE in different configurations and measuring the interaction forces. Experimental data are then compared to the optimization outcomes, proving that the proposed method provides contact wrench estimations consistent with the collected measurements and previous outcomes from the literature. Copyright 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data. Moreover, they can be computed for portions of the charging profile and real-world driving discharging conditions, facilitating real-time battery degradation estimation. The indicators are computed using experimental data from five cells aged under electric vehicle conditions, and a linear regression model is used to estimate the state of health. The results show that models trained with power autocorrelation and energy-based features achieve capacity estimation with maximum absolute percentage error within 1.5% to 2.5% .