This paper presents a comparative analysis of model-based control strategies for a Bionic Ankle Tensegrity Exoskeleton (BATE), designed to emulate the self-stress equilibrium and self-supporting characteristics of the human ankle biotensegrity structure. Model-based control strategies are conventional methods that can discover the principles of the BATE exoskeleton. The high dimensions and non-linearity of the BATE pose challenges for theoretical modeling and model-based control strategies. To address this, we propose a modeling method based on the force density that accounts for interaction forces. We evaluated the trajectory tracking performance and robustness of BATE under three power-assisted control methods: position control (PC), force control (FC) and hybrid force-position control (FPC). Experimental results demonstrate that the PC method offers superior performance in both trajectory tracking and robustness, making it suitable for early rehabilitation training to enhance flexibility. Our findings highlight the advantages of tensegrity exoskeletons over current wearable exoskeletons and introduce novel concepts for developing high-performance exoskeletons.

This paper proposes a novel Tensegrity-Based Fracture Reduction Robot (TFRR) designed to enhance the safety and efficacy of orthopedic procedures through integrated force-sensing and control capabilities. Inspired by the biomechanics of skeletal muscles, the robot adopts a tensegrity architecture that enables real-time monitoring of internal force distribution and dynamic adjustment of posture and inter-bone contact forces via controlled tensioning of its string network. To establish a theoretical foundation for system control, a comprehensive static analysis of the tensegrity structure is conducted, allowing accurate modulation of topological configurations through systematic tension control. Extensive experimental validation demonstrates the robustness and reliability of the proposed method across a range of operating conditions. In particular, targeted experiments on contact-force regulation confirm the robot’s ability to precisely monitor and adjust inter-bone forces during fracture reduction. These features collectively enable safer, more controlled surgical interventions, with the potential to reduce tissue trauma and improve clinical outcomes.

This paper presents a three-dimensional (3D) control framework for bevel-tip steerable needles that combines model predictive control (MPC) with hierarchical supervisory logic. The MPC layer uses a reduced-order two-mode switching model to generate the desired control actions, while the supervisory logic adaptively prioritizes in-plane and out-of-plane corrections based on real-time error magnitudes. This hierarchical approach smoothly modulates the axial rotation commands to minimize abrupt needle flips, thereby reducing the so-called "drilling effect", a key source of tissue trauma. The simulation results show that the proposed approach reduces tissue trauma by more than 50% compared to conventional pulse-width-modulated sliding mode controllers while achieving mean absolute error and targeting errors in the submillimeter range.

Modeling and controlling the musculoskeletal system are crucial for understanding human motor functions, optimizing human-robot interaction, and developing embodied intelligence. However, existing musculoskeletal models are mainly limited to specific body parts and muscle groups, and still face challenges in large-scale muscle coordination and the generation of diverse movements. In this study, we propose a musculoskeletal deep reinforcement learning (DRL) control model. This model integrates a Long Short-Term Memory (LSTM) network and a Multi-Head Self-Attention (MHSA) mechanism into the Proximal Policy Optimization (PPO) algorithm. The LSTM-MHSA-enhanced PPO control approach generates accurate muscle activation, motion trajectories, and torque control strategies to precisely control and replicate diverse human gaits based on target joint movements. Experimental results demonstrate that this LSTM-MHSA-enhanced PPO algorithm significantly improves the model accuracy compared to the traditional PPO algorithm, with a 43.75% and 34.14% reduction in Mean Absolute Error (MAE) for walking and running tasks, respectively. Furthermore, for complex tasks such as striking and dancing, the MAE decreases by 46.97% and 41.78%, respectively. These findings highlight that integrating LSTM and MHSA into PPO algorithm not only enhances gait simulation accuracy but also improves the model’s generalization capability, particularly for complex motion patterns. This research provides an efficient tool for motion simulation and gait analysis, advancing the development of human musculoskeletal control systems.

Advanced control algorithms are essential for enhancing the functionality of prosthetic hands, enabling them to operate in diverse conditions. This paper presents a Model Reference Adaptive Controller (MRAC) developed for a tendon-driven soft continuum wrist, integrated into the ’PRISMA HAND II’ prosthetic hand. The primary objective of our research is to design an adaptive controller that facilitates wrist movements eliminating external disturbances while minimizing computational requirements. To achieve this, kinematic and dynamic models of the wrist are developed based on the Piece-wise Constant Curvature (PCC) hypothesis. The controller consists of a reference model generated using the PCC model, and state errors are evaluated by comparing the responses of the reference model to those of the wrist model. These errors are reduced using the MRAC approach to make the wrist’s behavior closely align with that of the reference model. Stability of the closed-loop system is ensured using the Lyapunov direct method, along with the ’New Theorem of Stability’, a replacement for Barbalat’s lemma, ensuring that the error between the reference model and the actual system converges to zero and that the adaptive gains stabilize to fixed values. The adaptive performance of the controller is evaluated through experimental validations, where the motions of the prosthetic hand attached to the wrist are treated as unknown disturbances, and the mechanical stiffness of the wrist is considered as an uncertain parameter, resulting from the degradation of the internal springs.

Human beings adapt their motor patterns in response to their surroundings, utilizing sensory modalities such as visual inputs. This context-informed adaptive motor behavior has increased interest in integrating computer vision algorithms into robotic assistive technologies, marking a shift towardscontext aware control. However, such integration has rarely been achieved so far, with current methods mostly relying on data-driven approaches. In this study, we introduce a novel control framework for a soft hip exosuit, employing instead a physics-informed computer vision method grounded on geometric modeling of the captured scene for assistance tuning during stairs and level walking. This approach promises to provide a viable solution that is more computationally efficient and does not depend on training examples. Evaluating the controller with six subjects on a path comprising level walking and stairs, we achieved an overall detection accuracy of$93.0\pm 1.1\%$. Computer vision-based assistance provided significantly greater metabolic benefits compared to non-vision-based assistance, with larger energy reductions relative to being unassisted during stair ascent ($-18.9 \pm 4.1\%$vs.$-5.2 \pm 4.1\%$) and descent ($-10.1 \pm 3.6\%$vs.$-4.7 \pm 4.8\%$). Such a result is a consequence of the adaptive nature of the device, enabled by the context aware controller, that allowed for more effective walking support: i.e. the assistive torque showed a significant increase while ascending stairs ($+33.9\pm 8.8\%$) and decrease while descending stairs ($-17.4\pm 6.0\%$) compared to a condition without assistance modulation enabled by vision. These results highlight the potential of the approach, promoting effective real-time embedded applications in assistive robotics.

During certain human-robot collaboration tasks, the operator interacts with the robot by hand guidance to adjust the end-effector pose for spatial operations. The rotational operation is less intuitive to humans than translation. In fact, imagining the path to the target orientation is more challenging. In the literature related to control strategies for robot manual guidance, it is usually proposed to control translation and rotation independently. Our research explored and quantified the factors that influence operational intuition. A Virtual Fixture spatial guidance framework with intuition maintenance is proposed. This novel guidance scheme enables operators to effortlessly and simultaneously control both orientation and position in an intuitive way. High operation precision and efficiency can be achieved without interfering with the main task by exploring the null space with constraint optimization.

Soft continuum sections are widely used in robotic mechanisms for achieving dexterous motions. However, most available designs of soft continuum sections cannot support payload during a motion. This paper presents a development of a novel soft robotic wrist section for a prosthetic hand named ‘PRISMA Hand II’. Our research focuses on various phases of development of a soft continuum wrist section, that can support a substantial payload and maintain postures of the hand. Mechanical design, fabrication, and modelling strategies adopted for developing the wrist section are described. The design of the wrist section is constructed by assembling springs, discs, and tendons. The numbers and dimensions of springs and discs are optimised using static structural analysis. Kinematic modelling and dynamic modelling of the wrist section are carried out using Geometric Variable Strain (GVS) approach based on Cosserat rod theory and a generalised coordinate method respectively. The geometric formulations involved in Cosserat rod theory guaranteed accurate and quick computations considering deformation parameters. Dynamic modelling approach also enhanced the performance of the wrist section reducing errors and computational time during real time implementations. This paper also discusses about a dynamic model based controller strategy for the wrist section and advantages of the proposed controller are proved using a comparative study with a kinematic model based PID controller. Experimental validations of motions of the fabricated wrist section employing the dynamic controller are also included in the paper.

The differential mechanism can reduce the number of actuators and efficiently distribute force or power. We proposed a novel multiport output twisted string actuator (MO-TSA) with self-differential mechanism that employs a single actuator to achieve multiport outputs. The differential MO-TSA is adaptively controlled in accordance with the force differences at each output port, thus replacing the traditional differential gears and whiffletree mechanisms. Inspired by the hand muscles, we designed one hand glove using the MO-TSA, aiming to enhance the range of achievable grasp configurations. The hand glove is capable of performing various grasps with a single actuator, resulting in a lighter and simpler hand design and revolutionizing the field of twisted string actuators (TSAs) by offering a streamlined solution for achieving versatile actuation.

This paper presents a novel autonomous endoscope control method for the dVRK's Endoscopic Camera Manipulator (ECM), which allows the camera to track the surgical instruments on the Patient Side Manipulator (PSM). An Image-based Visual Servoing (IBVS) is enforced by the addition of a visibility constraint that ensures the identified surgical tool remains in the camera's Field Of View (FOV) for the continued availability of image feedback and a joint limits avoidance constraint that prevents the ECM from exceeding its joint limits. The work relies on an optimization approach, with constraints performed using the Control Barrier Functions concept (CBFs). The goal is to minimize the surgeon's cognitive and physical workload by removing the time-consuming job of camera reorientation, offering an enforced method compared to the traditional IBVS endoscopic camera controller.

Humans in contrast to robots are excellent in performing fine manipulation tasks owing to their remarkable dexterity and sensorimotor organization. Enabling robots to acquire such capabilities, necessitates a framework that not only replicates the human behaviour but also integrates the multi-sensory information for autonomous object interaction. To address such limitations, this research proposes to augment the previously developed kernelized synergies framework with visual perception to automatically adapt to the unknown objects. The kernelized synergies, inspired from humans, retain the same reduced subspace for object grasping and manipulation. To detect object in the scene, a simplified perception pipeline is used that leverages the RANSAC algorithm with Euclidean clustering and SVM for object segmentation and recognition respectively. Further, the comparative analysis of kernelized synergies with other state of art approaches is made to confirm their flexibility and effectiveness on the robotic manipulation tasks. The experiments conducted on the robot hand confirm the robustness of modified kernelized synergies framework against the uncertainties related to the perception of environment.

Modern robots act in dynamic and partially unknown environments where path replanning can be mandatory if changes in the environment are observed. Task-prioritized control strategies are well known and effective solutions to ensure local adaptation of robot behaviour. The highest priority in a stack of tasks is typically given to the management of correct robot operation or safe interaction with the environment such as obstacles or joint limits avoidance, that we can consider as constraints. If a constraint makes impossible achieving a certain task, such as tracking a Cartesian trajectory, a local control algorithm partially sacrifices the latter which is only accomplished to the best of the robot's ability to generate internal motions. In this control framework, problems may occur in some applications, like in the surgical domain, where it is not safe that some tasks are simply sacrificed without prior notice. The contribution of this work is to introduce a coordinate invariant index, that is used to provide a geometrical interpretation of task conflicts in a task-priority control framework and to develop a method for on-line detection of algorithmic singularities, with the goal of increasing safety and performances during robot operations.

Polyp dissection requires very accurate detection of the region of interest and high-precision cutting with adequate safety margins. Robot-assisted polyp dissection is a solution to accomplish high-quality intervention. This paper proposes a method to constrain the robot to follow an accurate dissection path based on Virtual Fixtures (VF). The VFs are created via specific control points obtained directly from images of the surgical scene and are updated by the vision algorithm. The VF constraints can autonomously adapt themselves to environment changing during the surgical intervention. The entire pipeline is validated through experiments on the da Vinci Research Kit (dVRK) robot.

During suturing tasks performed with minimally invasive surgical robots, configuration singularities and joint limits often force surgeons to interrupt the task and re-grasp the needle using dual-arm movements. This yields an increased operator's cognitive load, time-to-completion and performance degradation. In this paper, we propose a haptic-guided shared control method for grasping the needle with the Patient Side Manipulator (PSM) of the da Vinci robot avoiding such issues. We suggest a cost function consisting of (i) the distance from robot joint limits and (ii) the task-oriented manipulability along the suturing trajectory. Evaluating the cost and its gradient on the needle grasping manifold allows us to obtain the optimal grasping pose for joint-limit and singularity free robot movements during suturing. We compute force cues and display them through the Master Tool Manipulator (MTM) to guide the surgeon towards the optimal grasp. As such, our system helps the operator to choose a grasping configuration that allows the robot to avoid joint limits and singularities during post-grasp suturing movements. We show the effectiveness of the proposed haptic-guided shared control method during suturing using both simulated and real experiments. The results illustrate that our approach significantly improves the performance in terms of needle re-grasping.

In this paper, a grasping control strategy for an anthropomorphic robotic hand in a synergy-based framework is designed. The goal is to achieve a human-like behavior and robustness with respect to detailed information on objects shape and size. By adapting to underactuated kinematics a well-aimed method for human grasps mapping, the synergies subspace is computed. The designed control strategy allows the hand to adapt to object contours by means of coordinated fingers motion in the synergies subspace, and it is composed of a feedforward and two corrective terms to generate fingertip reference positions. The feedforward term is obtained by choosing, among the configurations contained in the set of grasps used for synergies computation, the one that is closest to the target grasp. This term ensures a suitable preshaping of the hand in the synergies subspace on the basis of object and grasp features. On the other hand, in order to adapt the grasp to the object, a weighted sum of two corrective terms is computed using the state of the motors as the feedback of the control law. The first term is designed to generate the closure of the hand towards the object. The second term is designed to optimize a grasp quality index based on force closure property. Moreover, exploiting the motor current feedback, soft synergies are implemented realizing compliance at the contact. The experiments conducted on the underactuated SCHUNK five-finger hand demonstrate the effectiveness of the method.

The papers in this special section focus on robotic manipulation based on bio-inspired computing. It is the goal of this papers to present applications of human manipulation ability in robotic systems, and to outline key strategies for robotic dexterous manipulation in next generation.

In this paper, a method for dexterous manipulation of 3D soft objects for real-time deformation control is presented, relying on Finite Element modelling. The goal is to generate proper forces on the fingertips of an anthropomorphic device during in-hand manipulation to produce desired displacements of selected control points on the object. The desired motions of the fingers are computed in real-time as an inverse solution of a Finite Element Method (FEM), the forces applied by the fingertips at the contact points being modelled by Lagrange multipliers. The elasticity parameters of the model are preliminarly estimated using a vision system and a force sensor. Experimental results are shown with an underactuated anthropomorphic hand that performs a manipulation task on a soft cylindrical object.

Automatic movement-prothesis control aims to increase the quality of life for patients with diseases causing temporary or permanent paralysis or, in the worst case, the lost of limbs. This technology requires the interaction between the user and the device through a control interface that detects the user's movement intention. Basing on the Motor-Imagery theory, many researchers have explored a wide variety of Classifiers to identify patients' physiological signals from many different sources in order to detect patients' moves intentions. We here propose a novel approach relying on the use of a Weightless Neural Network-based classifier, whose design lends itself to an easy hardware implementation. Additionally, we employ a non-invasive light weight and easy donning EEG-helmet in order to provide a portable controller interface. The developed interface is connected to a robotic hand for controlling open/close actions. We compared the proposed classifier with state of the art classifiers by showing that the proposed method achieves similar performance and contemporaneously represents a viable and practicable solution due to its portability on hardware devices, which will permit its direct implementation on the helmet board.

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Minimally invasive robotic surgery holds a fundamental role in modern surgery. However, one of its major limitations compared to classic laparoscopy is that the surgeon can only rely on visual perception, for the lack of haptic force feedback. A new solution for a force sensor placed at the end-tip of the trocar is presented here. This solution allows measuring the interaction forces between the surgical instrument and the environment without any changes to the instrument structure and with full adaptability to different robot platforms and surgical tools. A prototype of the sensor has been realized with 3D printed technology for a proof of concept. The static and dynamic characterization of the sensor is provided together with experimental validation.

The da Vinci Research Kit (DVRK) is a telerobotic surgical research platform endowed with an open controller that allows position, velocity and current control. We consider the problem of modelling and identification of both the Patient Side Manipulators (PSMs) and of the Master Tool Manipulators (MTMs) of the platform. This problem is relevant when realistic dynamic simulations have to be performed using standard software tools, but also for the design of model-based control laws, and for the implementation of sensorless strategies for collision detection or contact force estimation. A LMI-based approach is used for the identification of the robot dynamics in order to guarantee the physical feasibility of the parameters that is not ensured by standard least-squares methods. The identified models are validated experimentally.

In this work, a synergy-based reinforcement learning algorithm has been developed to confer autonomous grasping capabilities to anthropomorphic hands. In the presence of high degrees of freedom, classical machine learning techniques require a number of iterations that increases with the size of the problem, thus convergence of the solution is not ensured. The use of postural synergies determines dimensionality reduction of the search space and allows recent learning techniques, such as Policy Improvement with Path Integrals, to become easily applicable. A key point is the adoption of a suitable reward function representing the goal of the task and ensuring one-step performance evaluation. Force-closure quality of the grasp in the synergies subspace has been chosen as a cost function for performance evaluation. The experiments conducted on the SCHUNK 5-Finger Hand demonstrate the effectiveness of the algorithm showing skills comparable to human capabilities in learning new grasps and in performing a wide variety from power to high precision grasps of very small objects.

This paper presents an experimental study on human-robot comanipulation in the presence of kinematic redundancy. The objective of the work is to enhance the performance during human-robot physical interaction by combining Cartesian impedance modulation and redundancy resolution. Cartesian impedance control is employed to achieve a compliant behavior of the robot's end effector in response to forces exerted by the human operator. Different impedance modulation strategies, which take into account the human's behavior during the interaction, are selected with the support of a simulation study and then experimentally tested on a 7-degree-of-freedom KUKA LWR4. A comparative study to establish the most effective redundancy resolution strategy has been made by evaluating different solutions compatible with the considered task. The experiments have shown that the redundancy, when used to ensure a decoupled apparent inertia at the end effector, allows enlarging the stability region in the impedance parameters space and improving the performance. On the other hand, the variable impedance with a suitable modulation strategy for parameters' tuning outperforms the constant impedance, in the sense that it enhances the comfort perceived by humans during manual guidance and allows reaching a favorable compromise between accuracy and execution time.

This paper addresses the problem of controlling a robot arm executing a cooperative task with a human who guides the robot through direct physical interaction. This problem is tackled by allowing the end effector to comply according to an impedance control law defined in the Cartesian space. While, in principle, the robot's dynamics can be fully compensated and any impedance behaviour can be imposed by the control, the stability of the coupled human-robot system is not guaranteed for any value of the impedance parameters. Moreover, if the robot is kinematically or functionally redundant, the redundant degrees of freedom play an important role. The idea proposed here is to use redundancy to ensure a decoupled apparent inertia at the end effector. Through an extensive experimental study on a 7-DOF KUKA LWR4 arm, we show that inertial decoupling enables a more flexible choice of the impedance parameters and improves the performance during manual guidance.

In this paper, a method to derive the three predominant synergies and their temporal weights for planning grasps of the UB Hand IV (University of Bologna Hand, version IV) is proposed. The method adopted to define the postural synergies from experiments is based on the kinematic structure of the robotic hand and on the taxonomy of the grasps of common objects. The control strategy, exploiting postural synergies, that drives the hand during reach to grasp is further described. During prehension the hand moves continuously in a configuration space of highly reduced dimensionality with respect to its degrees of freedom. The experiments confirm that the UB Hand IV works efficiently in a synergy based framework for grasp planning and prehension control. It is shown that the introduction of the third predominant synergy significantly improves the grasping synthesis and performance, especially for the adduction/abduction motion of the thumb.

In this paper, the postural synergies configuration subspace given by the fundamental eigengrasps of the UB Hand IV (University of Bologna Hand, version IV) is derived through experiments. This study is based on the kinematic structure of the robotic hand and on the taxonomy of the grasps of common objects. Experimental results show that it is possible to obtain grasp synthesis for a large set of objects both in the case of precision or power grasps by using only a very limited set of dominant eigengrasps. The tasks here presented are planned with an initial hold of the hand followed by reach and grasp phases, that are unique for each object/grasp combination, during which the robotic hand posture evolves continuously within a subset of the hand configuration space given by the two predominant eigenpostures. The paper reports the method adopted to define from experiments the postural synergies for the UB Hand IV and the results of the grasp tasks performed adopting the defined synergies.

In this paper, we present a port-Hamiltonian model of a multi-fingered robotic hand, with soft-pads, while grasping and manipulating an object. The algebraic constraints of the interconnected systems are represented by a geometric object, called Dirac structure. This provides a powerful way to describe the non-contact to contact transition and contact viscoelasticity, by using the concepts of energy flows and power preserving interconnections. Using the port based model, an Intrinsically Passive Controller (IPC) is used to control the internal forces. Simulation results validate the model and demonstrate the effectiveness of the port-based approach.