Human locomotion has been extensively studied on flat ground; however, everyday walking often occurs on uneven terrain, which poses additional biomechanical challenges. The role of foot segmental mobility under these conditions remains underexplored. This study investigated how irregular surfaces influence foot kinematics and lower-limb biomechanics. Twelve able-bodied young adults performed eight barefoot tasks at self-selected speed, including level walking, obstacle crossing and walking on uneven ground. Multi-segment foot kinematics was quantified using the Oxford Foot Model, while hip, knee, and ankle kinematics and kinetics were analyzed using the lower-limb Plug-in Gait model to identify compensatory strategies. Participants exhibited increased plantarflexion of the hallux and forefoot, increased dorsiflexion of the forefoot and hindfoot relative to the tibia, and a reduced range of motion of the foot segments, suggesting a stiffening strategy to enhance grip and stability. Ankle showed increased dorsiflexion, while knee and hip flexion increased throughout stance. Kinetically, ankle plantarflexor torque increased during the first half of stance, whereas push-off torque and power were reduced. Concurrently, the knee exhibited increased energy dissipation during push-off, while a tendency toward greater power generation is shown during most of the single-support phase. Overall, these findings highlight a redistribution of joint function and the pivotal role of foot segments kinematics and ankle dynamics in enabling stable locomotion on uneven terrain.

Modular continuum soft arms represent an emerging class of robotic systems characterized by flexible, highly deformable structures. Designing shape controllers for these arms poses significant challenges due to their modeling complexity and hyper-redundant nature. Our goal is to develop a scalable control framework for modular arms, where each module is self-contained. Starting from distributed control theory, we assign a collaborative controller to each soft module. Through collaboration among modules, the framework enables the system to achieve the desired tip position and shape. Each controller relies on the minimal model, such as the Constant Curvature, of its self-contained module and the local transformation shared by adjacent modules. We present three kinematic control strategies - Consensus, Bipartite Consensus, and Formation Control - for a modular continuum soft arm, that progressively relax constraints to achieve more complex, adaptable shapes. In addition, we develop a decentralized curvature-based dynamic controller to manage dynamic coupling among modules. The validation is carried out through numerical analysis and dynamic simulations of soft arms with varying numbers of modules.

The flat, stiff sole of energy-storage-and-return prosthetic feet hinders adaptation to irregular terrains. This is among the causes both of high falling risk and of the consequential arising compensatory mechanisms in prosthetic users. To overcome that, we introduce the SoftFoot Pro, an anthropomorphic and adaptive prosthetic foot featuring a flexible and inextensible sole that passively adapts to obstacles, widening the ground contact area. Experimental comparison to a traditional carbon fibre foot in two unilateral transtibial prosthetic users highlights that the adaptive design reduces stance torque and power consumption at the contralateral knee and at both hips, both on level and uneven grounds. The more even load distribution between the two limbs reduces compensatory strategies and gait asymmetries, resulting in biomechanics closer to that of unimpaired individuals. These findings hold promise for enhancing quality of life for individuals with limb loss, potentially improving stability and reducing fall risk. Irregular terrains challenge users of classic prosthetic feet. Here, the authors introduce an adaptive prosthetic foot that equalizes ground contact pressure and reduces compensatory strategies and gait asymmetries compared with a traditional carbon fibre foot.

With the growing global demand for plant production, the agricultural sector faces the urgent challenge of developing innovative, efficient, and sustainable solutions. Within this context, innovative approaches are essential for advancing sustainable farming, addressing labor shortages, and enhancing a variety of tasks across the agricultural product life cycle. Concentrating on viticulture, this study incorporates direct feedback from end users, highlighting key needs: multifunctional robotic systems designed for precision tasks in grape production, and a particular focus on vineyard monitoring to enable selective interventions on individual grape clusters. This emphasis is driven by the fact that monitoring and maintenance activities extend throughout the year, a perspective strongly supported by practitioners in the field. This study explores the design, control, and evaluation of a multifunctional robotic system for precision agricultural tasks specific to vineyard environments, enabling precision agriculture through the accurate localization of individual grape clusters by integrating GPS data, onboard sensor information, and robot kinematics. The proposed platform combines custom and commercial components across three core modules: navigation, which includes path planning and obstacle avoidance; perception for detecting and localizing grape positions within the vineyard; and manipulation, enabling the robot to carry out inspections, targeted treatments, and interact with grape clusters. A user-friendly graphical interface is implemented to allow operators without robotics expertise to manage the software modules effortlessly and oversee task execution. Experimental validation was conducted in both artificial and real vineyard settings, demonstrating the potential of autonomous robotic platforms to promote more efficient and sustainable viticulture practices. Feedback from stakeholders are presented both in terms of initial needs specification and post-deployment outcomes, providing to the reader an idea on solutions and actions that can drive progress in this field.

Objective: Upper limb loss due to traumatic injury or disease poses significant challenges to autonomy, daily function, and workforce reintegration, profoundly impacting overall quality of life. While myoelectric prosthetic hands have the potential to restore dexterity, many users discontinue use due to limited functionality and durability.This manuscript describes the design and rationale of an ongoing clinical trial aimed at addressing these gaps in real-world settings. Methods: We searched for completed and ongoing clinical trials on ClinicalTrials.gov to study their structure and their gaps, and then we presented the protocol of our ongoing clinical trial. This protocol outlines a randomized crossover clinical trial enrolling 36 adults with upper limb loss to evaluate two multi-articulated myoelectric prosthetic hands. Results: Our review of clinical trials revealed that the unique strength of our design is the integration of standardized laboratory tests, extended daily use, onboard usage data, and validated satisfaction surveys. We provided a detailed description of all design choices and rationale of the ongoing clinical study. Conclusion: The comparison between our design and the design of other studies indicates that our design is unique in the integration of biomechanical assessments, real-world usage monitoring, and user-reported outcomes. This clinical trial should be capable of assessing if one specific device design can offer clinically meaningful advantages over another. Significance: The design of our clinical trial could inform the design of clinical trials targeting the optimization of prostheses and their acceptance by prosthetic users.

This paper tackles the challenges of automating battery removal from small electronic devices, such as heat cost allocators and smoke detectors. The process is critical for mitigating fire hazards caused by lithium batteries in recycling facilities and for supporting a circular economy. We focus on advanced methodologies and robotic technologies designed to overcome the significant hurdles posed by the diverse range of device designs, complex battery compartments, and varying states of damage. Our approach integrates Vision-Language Models (VLMs) for real-time, adaptive disassembly planning, computer vision, tactile skills, soft robotics, and reconfigurable robotic workcells to enhance perception, dexterity, and adaptability in handling diverse device designs and damage states. Additionally, a reconfigurable robotic workcell with modular hardware and standardized interfaces enables seamless adaptation to various devices. Laboratory testing demonstrates improved efficiency and reduced manual intervention, highlighting the potential of AI-driven, reconfigurable robotics for scalable and sustainable e-waste recycling.

Skin stretch across hand and fingers joints plays a crucial role in shaping human proprioception. Previous work has characterized the time-varying relationship between joint angles during grasping tasks and the corresponding skin stretch across the Proximal Interphalangeal (PIP) and Metacarpophalangeal (MCP) joints via linear regression. In this paper, we investigate whether, and to what extent, sparse skin-stretch information can encode hand kinematics during grasping tasks. We leverage a previously introduced dataset and propose the use of functional Principal Component Analysis (fPCA) to identify a set of basis functions whose linear combinations reconstruct both joint-angle and skin-stretch time series. These functional components enable the estimation of an a priori covariance structure, which can be fused with insufficient skin-stretch measurements, within a minimum-variance estimation framework, to reconstruct the full hand kinematic and skin-stretch state. We also address the problem of identifying which skin-stretch measurements are the most informative for reconstructing hand poses. Our results show that, by considering only as few as three skin-stretch measurements, hand kinematics across up to ten finger joints can be reconstructed with a maximum Normalized Root-Mean-Square Error (NRMSE) below 9.1

Over the past decade, the adaptive synergy paradigm for robot hand design has combined neuroscientific insight on postural synergies with soft robotics to enable versatile grasping. The Pisa/IIT SoftHand exemplified the effectiveness of this approach in many applications, but also revealed limitations in scalability, durability, and efficiency, which are critical factors for real-world deployment. This work presents a radically new mechanical design for tendon-driven underactuated hands that overcomes these limitations, while preserving the inspiring principles of the paradigm. The new design supports integration of one or two synergistic motion patterns, mathematically validated within the augmented adaptive synergies framework. We employ a modular, customizable design approach, enabling various hand configurations from a minimal set of parts. This versatility supports variegated applications in industry, rehabilitation, and field robotics, streamlining production and reducing costs. Experimental results provide a quantitative characterization of grasping performance and demonstrate that the novel SoftHand preserves its passive adaptability and in-hand manipulation capabilities. Crucially, the novel layout achieves substantial scalability, allowing miniaturization to 50% of the original size and weight. Furthermore, it enhances durability and efficiency through reduced friction, resulting in increased lifetime by 930% and a 360% improvement in battery life.

As with every emerging technology, new tools in the hands of artists reshape the nature of artwork creation. Current frameworks for robotics in arts deploy the robot as an autonomous creator or a collaborator, thus leaving a certain gap between the human artist and the machine. Now, we stand at the dawn of an era where artists can escape physical limitations and reshape their creative identity by inhabiting an alternative body. This new paradigm allows artists not only to command a robot remotely, but also to be a robot, to see and feel through it, experiencing a new embodied reality. Unlike virtual reality, where art is created in a digital dimension, in this case art creation is still firmly grounded in the material world: clay molded by mechanical hands, paint swept across a canvas or gestures performed on a physical stage alongside human actors. Through the robot avatar Alter-Ego, we explore the Alter-Art paradigm in dance, theater, and painting; it integrates immersive teleoperation and compliant actuation to enable a first-person creative experience. Analyzing qualitative artistic feedback, we investigate how embodiment shapes creative agency, identity and interaction with the environment. Our findings suggest that artists rapidly develop a sense of presence within the robotic body. The robot's physical constraints influence the creative process, manifesting differently across artistic domains. We highlight embodiment as a central design principle, contributing to social robotics and expanding the possibilities for telepresence and accessible artistic expression.

Following limb amputation and targeted muscle reinnervation (TMR), nerves that originally innervated agonist and antagonist muscles are rerouted into one or more residual target muscles. This rerouting profoundly alters the natural mechanical coupling and afferent signalling that normally link muscle groups in intact limbs. Despite this disruption, in this study we demonstrate, using high-density intramuscular microelectrode arrays implanted in reinnervated muscles of three TMR participants, that motor units (MUs) associated with agonist and antagonist tasks remain functionally coupled. Specifically, over 40

We address imitation-based trajectory generalization for robots subject to nonholonomic constraints. Standard Dynamic Movement Primitives (DMPs) are effective for unconstrained systems, but they can generate infeasible trajectories when applied to wheeled robots such as unicycles or car-like vehicles. We introduce Nonholonomic Dynamic Movement Primitives (NHDMPs), a framework that reformulates the imitation objective in quasi-velocity space and replaces the standard linear attractor with a Lyapunov-stable controller that enforces nonholonomic feasibility, together with two forcing terms learned from demonstrations. We derive a numerical optimization-based reference benchmark and evaluate two NHDMP variants: an optimized version with per-instance parameter optimization and a trained version with fixed parameters learned offline. On 1,000 random trajectories, the optimized variant achieves imitation discrepancy close to the numerical optimum while being an order of magnitude faster; the trained variant achieves comparable trajectory quality at lower computational cost, outperforming prior nonholonomic DMP methods on the considered benchmark. Physical experiments on the AlterEgo wheeled platform, together with simulations of sequential primitive chaining for car-like road navigation, demonstrate the approach across systems of increasing complexity.

There is strong evidence that skin stretch at the joint contributes to proprioception, the perceptual representation of body position and motion. Previous studies on fingers and hand dorsum focused on illusory movement elicited by skin-stretch stimuli when no actual motion was performed, often combined with local anesthesia and mostly delivered qualitatively, leaving unclear how controlled, augmented skin deformation influences proprioception during active movement. Here, we addressed this question by applying precisely scaled skin-stretch stimuli across the proximal interphalangeal (PIP) joint of the index finger, amplifying naturally occurring deformations during finger flexion. Participants performed an active hand position-matching task with their bare hand and while wearing a custom, non-invasive tactile device capable of controlled skin-stretch stimulation. While no significant differences were observed between the bare hand and the device-deactivated condition, augmenting natural skin stretch consistently shifted perceived finger posture toward more extended configurations. This finding demonstrates that cutaneous deformation can systematically reshape proprioceptive estimates even when muscle spindles and efferent signals are engaged, revealing a continuous integration of tactile and musculoskeletal cues in kinesthetic perception. These results advance our understanding of skin-stretch contribution to proprioceptive processing in active tasks, with implications for haptics-based human-machine interaction and virtual reality applications.

When improving an upper-limb prosthetic system, choosing whether to prioritise visual design or haptic performance becomes crucial and offers valuable insights for advancing prosthesis development. This paper investigates the influence of multisensory feedback on prosthetic embodiment, focusing on key components of embodiment, including ownership, multisensory, and agency, through two Virtual Reality (VR) experiments. Our first study involved 24 participants without limb loss in a target-reaching task using different visual renderings (glove, realistic, prosthetic hands) and haptic feedback modalities (none, vibrotactile, pressure, combined). Results showed that visual appearance was a powerful determinant across all components. Realistic hands produced the highest embodiment scores, whereas the prosthetic hand representation significantly degraded them. Haptic feedback substantially affected ownership and multisensory scores; pressure feedback improved ownership compared to no feedback, and all haptic conditions elevated multisensory scores. A subsequent experiment with 12 new participants explored the effect of haptic feedback location (wrist, forearm, upper arm, contralateral forearm). This experiment found no significant performance differences among locations, though co-located feedback was preferred, where the visual feedback in VR appeared at the same spatial position as the perceived tactile contact. Finally, a pilot study with a prosthetic user provided preliminary support for the relevance of realistic visual appearance and multimodal haptic feedback. Overall, results suggest that visual realism and haptic feedback support different dimensions of prosthetic embodiment, with visual realism mainly enhancing ownership and haptic feedback strengthening multisensory. Importantly, concurrent vibrotactile-pressure feedback emerged as a promising and well-accepted solution, providing richer interaction cues without compromising embodiment.

Walking is commonly seen as a simple and effortless activity. In reality, it arises from a complex and intricately coordinated neurome-chanical system. To effectively assist or replicate this process, artificial devices must adapt seamlessly to the dynamic goals and environments of their users. Virtual environments provide an accessible and low-risk way to accelerate the early-stage development of these technologies. To capture the challenges inherent in non-steady-state locomotion, these simulations must reproduce the complexities of movements and environments. Here, we present a locomotion synthesis system that allows us to simulate non steady-state walking on uneven surfaces and obstacles with quantitative measures of kinematics, kinetics and stability. We applied this environment to trial a prototype compliant prosthetic foot, and compared the gait resulting from its use with that of a rigid foot. We contrasted our results with an experimental study with a real prosthesis user and corroborated potential benefits that compliance can provide during obstacle navigation. Our results demonstrate the application of simulated locomotion agents as subjects in a virtual gait lab. This approach could lower the participation burden on the patient population when iterating novel concepts for the design of prosthetic devices.

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 article 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. In addition, 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 degrees C$ range of motion, achieves the target stiffness range of [2, 60] N center dot m/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.

Motor impairments, particularly spinal cord injuries, impact thousands of people each year, resulting in severe sensory and motor disabilities. Assistive technologies play a crucial role in supporting these individuals with activities of daily living. Among such technologies, body–machine interfaces (BoMIs) are particularly important, as they convert residual body movements into control signals for external robotic devices. The main challenge lies in developing versatile control interfaces that can adapt to the unique needs of individual users. This study aims to adapt for people with spinal cord injury a novel control framework designed to translate residual user movements into commands for the humanoid robot Alter-Ego. After testing and refining the control algorithm, we developed an experimental protocol to train users to control the robot in a simulated environment. A total of 12 unimpaired participants and two individuals affected by spinal cord injury participated in this study, which was designed to assess the system’s applicability and gather end-user feedback on its performance in assisting with daily tasks. Key metrics such as the system’s usability, accuracy, performance, and improvement metrics in navigation and reaching tasks were assessed. The results suggest that assistive robots can be effectively controlled using minimal residual movements. Furthermore, structured training sessions significantly enhance overall performance and improve the accuracy of the control algorithm across the selected tasks.

Skin stretch feedback is increasingly recognized in rehabilitation and prosthetics for its ability to convey proprioceptive cues. Magnetically-induced skin stretch can offer interesting perspectives, not only for non-invasive stimulation but also for integration within the body. This study presents the first step towards the development of the MISS (Magnetically Induced Skin Stretch) device, a wearable system that uses magnetic interactions to provide proprioceptive feedback through skin stretch. By manipulating two magnets attached to the skin, the device aims to convey stretch sensations for hand proprioception. After preliminary Finite Element Method Magnetics simulations (FEMM), we came to the MISS design, which consists of a C-shaped ferromagnetic core with two solenoids at its ends. When the solenoids are powered, they generate a magnetic field that interacts with the magnets, causing controlled skin displacements to produce both stretch and pinch sensations. Psychophysical tests with the device positioned on the hand dorsum were performed to evaluate the skin deformations, whose outcomes support the device's potential application in proprioceptive feedback for prosthetics and hand rehabilitation.

In contemporary studies within the upper-limb prosthetic field, current investigations persistently revolve around the research of innovative control methodologies. These works are driven by the intention to diminish cognitive fatigue experienced by users while simultaneously enhancing the resilience and intuitiveness of control mechanisms. Several investigations have introduced approaches involving the exploitation of corrective movements as indicators of control system error, effectively closing the control loop. In prior studies, a predetermined linkage associates specific compensatory motions with distinct degrees of freedom in prosthetic devices. In contrast to this precedent, our study introduces a methodology that circumvents this intermediary step, enabling a direct mapping between human motions and the number of prosthetic joints. The proposed algorithm has been instantiated and validated using Matlab/Simulink, employing a simulated scenario featuring a trans-humeral prosthetic user.

Endowing robots with advanced tactile abilities based on biomimicry involves designing human-like tactile sensors, computational models, and motor control policies to enhance contact information retrieval. Here, we consider compliance discrimination with a soft biomimetic tactile optical sensor (TacTip). In previous work, we proposed a vision-based approach derived from a computational model of human tactile perception to discriminate object compliance with the TacTip, based on contact area spread computation over the indenting force. In this work, we first increased the robustness of our vision-based method with a more precise estimation of the initial contact area condition, which enables correct compliance estimation also when the probing direction is other than normal to the specimen surface. Then, we integrated within our validated framework the mechanisms of internal muscular regulation (co-contraction) that humans adopt during object compliance probing, to maximize the information uptake. To this aim, we used human co-contraction patterns extracted during object softness probing to control a Variable Stiffness Actuator (that emulates the agonistic-antagonistic behavior of human muscles), which is used to actuate the indenter system endowed with the TacTip for object compliance exploration. We found that our model-based approach for compliance discrimination, fed with more precisely estimated initial conditions, significantly improves with the human-inspired impedance regulation, with respect to the usage of a rigid actuator.

Due to their fast and efficient locomotion, two-wheeled humanoids are fascinating systems with the potential to be involved in many application domains, including healthcare, manufacturing, and many others. However, these robots constitute a challenging case of study for control purposes due to the two-wheeled inverted pendulum dynamics that characterizes their mobility and support, as it is underactuated and unstable. In this article, we propose a novel whole-body control approach to stabilize two-wheeled humanoids. To tackle the control problem of their forward motion and pitch equilibrium, leveraging on the observation that such systems are usually characterized by a faster and a slower dynamics (being the pitch angle faster and the forward displacement slower), we design a composite whole-body control that combines two computed-torque control loops to stabilize both dynamics to the desired trajectories. The control approach is introduced and its derivation is described for the simpler case of a two-wheeled inverted pendulum first, and for a whole two-wheeled humanoid after. To prove its validity, the control approach is tested experimentally on the two-wheeled humanoid robot Alter-Ego. The robot proves to be able to perform complicated interaction tasks, including opening a door, grasping a heavy object, and resisting to external dynamic disturbances.