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Joint absence in people with upper-limb-difference leads to compensatory motions. Such compensation has long been a topic of study, but typically only for a single object/user layout, which is unlikely to spatially generalize. We seek to understand how motion varies over a planar workspace for different target orientations and wrist mobility conditions. We therefore present a study that records arm and torso pose during grasping of 49 equally spaced cylindrical targets. Furthermore, we seek to validate the research practice of using wrist-immobilizing bypass sockets on able-bodied participants to simulate prostheses without wrists. Participants were 2 transradial amputees and 7 able-bodied individuals who conducted the study with and without wrist braces, generating 2450 trajectories. Heat-maps illustrate variation over the workspace in Mean Joint Angle, Range of Joint Motion and Distance Travelled by Body Segment. Results indicate that greater wrist restriction primarily exacerbated shoulder internal rotation and elbow flexion, not the trunk. We observed that bypass sockets do not fully simulate amputee behavior. Furthermore, amputee reaching with their intact limb is different to the reaching motion of normative participants, implying that transradial limb-difference affects both sides of the body. Differences in participant behavior were also observed between horizontal and vertical target orientations.

It's been repeatedly shown that pictures of graspable objects can facilitate visual processing, even in the absence of reach-to-grasp actions, an effect often attributed to the concept of affordances. A classic demonstration of this is the handle compatibility effect, characterised by faster reaction times when the orientation of a graspable object's handle is compatible with the hand used to respond, even when the handle orientation is task-irrelevant. Nevertheless, it is debated whether the speeded reaction times are a result of affordances or spatial compatibility. First, we investigated whether we could replicate the handle compatibility effect while controlling for spatial compatibility. Participants (N = 68) responded with left or right-handed keypresses to whether the object was upright or inverted and, in separate blocks, whether the object was red or green. We failed to replicate the handle compatibility effect, with no significant difference between compatible and incompatible conditions, in both tasks. Second, we investigated whether there is a lower visual field (VF) advantage for the handle compatibility effect in line with what has been found for hand actions. A further 68 participants responded to object orientation presented either in the upper or lower VF. A significant handle compatibility effect was observed in the lower VF, but not the upper VF. This suggests that there is a lower VF advantage for affordances, possibly as the lower VF is where our actions most frequently occur. However, future studies should explore the impact of eye movements on the handle compatibility effect and tool affordances. [ABSTRACT FROM AUTHOR]

The ability to hold objects relies on neural processes underlying grip force control during grasping. Brain activity lateralized to contralateral hemisphere averaged over trials is associated with grip force applied on an object. However, the involvement of neural variability within-trial during grip force control remains unclear. We examined dependence of neural variability over frontal, central, and parietal regions of interest (ROI) on grip force magnitude using noninvasive electroencephalography (EEG). We utilized our existing EEG dataset comprised of healthy young adults performing an isometric force control task, cued to exert 5, 10, or 15% of their maximum voluntary contraction (MVC) across trials and received visual feedback of their grip force. We quantified variability in EEG signal via sample entropy (sequence-dependent) and standard deviation (sequence-independent measure) over ROI. We found lateralized modulation in EEG sample entropy with force magnitude over central electrodes but not over frontal or parietal electrodes. However, modulation was not observed for standard deviation in the EEG activity. These findings highlight lateralized and spatially constrained modulation in sequence-dependent, but not sequence-independent component of EEG variability. We contextualize these findings in applications requiring finer precision (e.g., prosthesis), and propose directions for future studies investigating role of neural entropy in behavior. [ABSTRACT FROM AUTHOR]

Currently, multi-posture robots have complex grasping robotic manipulators with low power density, making it difficult to miniaturize and integrate. In this paper, a multi-posture grasping manipulator actuated by shape memory alloy with different functional modules is presented. It is composed of deflection, translation, rotation and grasping modules. Based on a D-H parameter method, the end motion trajectory model is established and the end motion space is drawn. Finally, the grasping experiment of a light circular object is carried out to verify the validity of the multi-posture grasping function of the multi-module combination manipulator, which provides a choice for future intelligent robot manipulators. [ABSTRACT FROM AUTHOR]

With the advent of robotics and artificial intelligence, the potential for automating tasks within human-centric environments has increased significantly. This is particularly relevant in the retail sector where the demand for efficient operations and the shortage of labor drive the need for rapid advancements in robot-based technologies. Densely packed retail shelves pose unique challenges for robotic manipulation and detection due to limited space and diverse object shapes. Vacuum-based grasping technologies offer a promising solution but face challenges with object shape adaptability. The study proposes a framework for robotic grasping in retail environments, an adaptive vacuum-based grasping solution, and a new evaluation metric—termed grasp shear force resilience—for measuring the effectiveness and stability of the grasp during manipulation. The metric provides insights into how retail objects behave under different manipulation scenarios, allowing for better assessment and optimization of robotic grasping performance. The study's findings demonstrate the adaptive suction cups' ability to successfully handle a wide range of object shapes and sizes, which, in some cases, overcome commercially available solutions, particularly in adaptability. Additionally, the grasp shear force resilience metric highlights the effects of the manipulation process, such as in shear force and shake, on the manipulated object. This offers insights into its interaction with different vacuum cup grasping solutions in retail picking and restocking scenarios. [ABSTRACT FROM AUTHOR]

Introduction: There is currently a lack of easy-to-use and effective robotic devices for upper-limb rehabilitation after stroke. Importantly, most current systems lack the provision of somatosensory information that is congruent with the virtual training task. This paper introduces a novel haptic robotic system designed for upper-limb rehabilitation, focusing on enhancing sensorimotor rehabilitation through comprehensive haptic rendering. Methods: We developed a novel haptic rehabilitation device with a unique combination of degrees of freedom that allows the virtual training of functional reach and grasp tasks, where we use a physics engine-based haptic rendering method to render whole-hand interactions between the patients' hands and virtual tangible objects. To evaluate the feasibility of our system, we performed a clinical mixed-method usability study with seven patients and seven therapists working in neurorehabilitation. We employed standardized questionnaires to gather quantitative data and performed semi-structured interviews with all participants to gain qualitative insights into the perceived usability and usefulness of our technological solution. Results: The device demonstrated ease of use and adaptability to various hand sizes without extensive setup. Therapists and patients reported high satisfaction levels, with the system facilitating engaging and meaningful rehabilitation exercises. Participants provided notably positive feedback, particularly emphasizing the system's available degrees of freedom and its haptic rendering capabilities. Therapists expressed confidence in the transferability of sensorimotor skills learned with our system to activities of daily living, although further investigation is needed to confirm this. Conclusion: The novel haptic robotic system effectively supports upper-limb rehabilitation post-stroke, offering high-fidelity haptic feedback and engaging training tasks. Its clinical usability, combined with positive feedback from both therapists and patients, underscores its potential to enhance robotic neurorehabilitation. [ABSTRACT FROM AUTHOR]

Deep Reinforcement Learning applications are growing due to their capability of teaching the agent any task autonomously and generalizing the learning. However, this comes at the cost of a large number of samples and interactions with the environment. Moreover, the robustness of learned policies is usually achieved by a tedious tuning of hyper-parameters and reward functions. In order to address this issue, this paper proposes an evolutionary RL algorithm for the adaptive optimization of hyper-parameters. The policy is trained using an on-policy algorithm, Proximal Policy Optimization (PPO), coupled with an evolutionary algorithm. The achieved results demonstrate an improvement in the sample efficiency of the RL training on a robotic grasping task. In particular, the learning is improved with respect to the baseline case of a non-evolutionary agent. The evolutionary agent needs 60 % fewer samples to completely learn the grasping task, enabled by the adaptive transfer of knowledge between the agents through the evolutionary algorithm. The proposed approach also demonstrates the possibility of updating reward parameters during training, potentially providing a general approach to creating reward functions. [ABSTRACT FROM AUTHOR]

The realism and immersion of Virtual Reality (VR) experiences depend on the quality of interactions between the virtual hand and virtual objects. The current drawbacks, such as seemingly artificial hand-object interactions and erratic post-collision behaviors of both virtual objects and the virtual hand, curtail the effectiveness of VR in tasks requiring precise manipulation, precluding sustained and successful adoption of VR in precision manual tasks. To address these limitations, we advocate a strategic approach across three related domains: (1) Developing a sufficiently realistic virtual hand model (i.e., a set of rigid bodies or deformable meshes) that can implement the complex movements of a biological hand. (2) Exploiting synergistic patterns of multi-digit motion and contact forces revealed by research on neuroscience, psychophysics, and manual actions to develop hand-object collision handling algorithms. (3) Implementing seamless and fluid releases of whole-hand virtual grasps, especially involving complex grasps and in-hand manipulation tasks. This article explores various aspects of virtual grasping that go beyond traditional physics simulations, proposing innovative solutions to overcome technical barriers across these domains. [ABSTRACT FROM AUTHOR]

The simple act of viewing and grasping an object involves complex sensorimotor control mechanisms that have been shown to vary as a function of multiple object and other task features such as object size, shape, weight, and wrist orientation. However, these features have been mostly studied in isolation. In contrast, given the nonlinearity of motor control, its computations require multiple features to be incorporated concurrently. Therefore, the present study tested the hypothesis that grasp computations integrate multiple task features superadditively in particular when these features are relevant for the same action phase. We asked male and female human participants to reach-to-grasp objects of different shapes and sizes with different wrist orientations. Also, we delayed the movement onset using auditory signals to specify which effector to use. Using electroencephalography and representative dissimilarity analysis to map the time course of cortical activity, we found that grasp computations formed superadditive integrated representations of grasp features during different planning phases of grasping. Shape-by-size representations and size-by-orientation representations occurred before and after effector specification, respectively, and could not be explained by single-feature models. These observations are consistent with the brain performing different preparatory, phase-specific computations; visual object analysis to identify grasp points at abstract visual levels; and downstream sensorimotor preparatory computations for reach-to-grasp trajectories. Our results suggest the brain adheres to the needs of nonlinear motor control for integration. Furthermore, they show that examining the superadditive influence of integrated representations can serve as a novel lens to map the computations underlying sensorimotor control. [ABSTRACT FROM AUTHOR]

Soft robotic devices are designed for applications such as exploration, manipulation, search and rescue, medical surgery, rehabilitation, and assistance. Due to their complex kinematics, various and often hard-to-define degrees of freedom, and nonlinear properties of their material, designing and operating these devices can be quite challenging. Using tools such as optimization methods can improve the efficiency of these devices and help roboticists manufacture the robots they need. In this work, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of soft robots, particularly focusing on literature exploiting evolutionary computation (EC). We completed the search in the IEEE, ACM, Springer, SAGE, Elsevier, MDPI, Scholar, and Scopus databases between 2009 and 2024 using the keywords "soft robot," "design," and "optimization." We categorized our findings in terms of the type of soft robot (i.e., bio-inspired, cable-driven, continuum, fluid-driven, gripper, manipulator, modular), its application (exploration, manipulation, surgery), the optimization metrics (topology, force, locomotion, kinematics, sensors, and energy), and the optimization method (categorized as EC or non-EC methods). After providing a road map of our findings in the state of the art, we offer our observations concerning the implementation of the optimization methods and their advantages. We then conclude our paper with suggestions for future research. [ABSTRACT FROM AUTHOR]

Tools enable humans to extend their sensing abilities beyond the natural limits of their hands, allowing them to sense objects as if they were using their hands directly. The similarities between direct hand interactions with objects (hand-based sensing) and the ability to extend sensory information processing beyond the hand (tool-mediated sensing) entail the existence of comparable processes for integrating tool- and hand-sensed information with vision, raising the question of whether tools support vision in bimanual object manipulations. Here, we investigated participants' performance while grasping objects either held with a tool or with their hand and compared these conditions with visually guided grasping (Experiment 1). By measuring reaction time, peak velocity, and peak of grip aperture, we found that actions were initiated earlier and performed with a smaller peak grip aperture when the object was seen and held with the tool or the contralateral hand compared to when it was only seen. Thus, tool-mediated sensing effectively supports vision in multisensory grasping and, even more intriguingly, resembles hand-based sensing. We excluded that results were due to the force exerted on the tool's handle (Experiment 2). Additionally, as for hand-based sensing, we found evidence that the tool supports vision by mainly providing object positional information (Experiment 3). Thus, integrating the tool-sensed position of the object with vision is sufficient to promote a multisensory advantage in grasping. Our findings indicate that multisensory integration mechanisms significantly improve grasping actions and fine-tune contralateral hand movements even when object information is only indirectly sensed through a tool. Public Significance Statement: Tools allow extending the hands' sensing capabilities beyond their anatomical limits. Here, we show that object information sensed through a tool can guide bimanual object manipulations as effectively as when directly sensed by the hand. Both tool-based and hand-mediated sensing provide relevant object positional information that is merged with vision to improve action performance. Our findings provide evidence about the interchangeable use of tools and hands for skilled actions and open new perspectives for prosthetic applications and rehabilitative plans. [ABSTRACT FROM AUTHOR]

The end-state comfort effect (ESC) describes the tendency to grasp an object with an initial uncomfortable grasp posture in order to achieve a comfortable end posture. The ESC is an example for anticipative processes in manual action. ESC planning is investigated in many studies where this effect is measured in the context of motor observation and motion capture. However, there is little evidence if the anticipative link between different action states, especially between initial grasp postures and comfortable end postures, is represented in memory. The aim of the present study was to investigate whether the perception of a grasp posture holding a bar leads to the activation of actionrelated representations of grasping actions. For this purpose, a priming paradigm was used in which prime images were shown depicting either a comfortable (overhand grip) or uncomfortable (underhand grip) grasp posture holding a two-colored bar. The subsequently shown target images represented either a comfortable (thumb-up) or uncomfortable (thumb-down) final grasp posture of this grasping action. Due to the different grasp postures in the prime and target, prime-target pairs represented different types of action sequences. Furthermore, physically possible, and physically impossible actions were presented. Participants were asked to react to the top color of the bar shown in the target-picture, whereby the shown grasp posture was irrelevant for this decision. Results showed that reaction times did not differ after presentation of an overhand grip to target pictures showing comfortable or uncomfortable final grasp postures. In contrast, after presentation of an underhand grip in the prime, reactions to target pictures with final comfortable grasp postures were faster compared to target pictures with uncomfortable grasp postures. The effect was only found for the physically possible action. The findings suggest that the perception of the underhand grip leads to cognitive pre-activation of a final action state. The present study suggests that the association between an initial uncomfortable underhand grip and its action effect, in form of a final action state that is consistent with the ESC, is represented in memory. Such motor representation might be important for the anticipation and control of goal-directed grasping. [ABSTRACT FROM AUTHOR]

The handshake is the most acceptable gesture of greeting in many cultures throughout many centuries. To date, robotic arms are not capable of fully replicating this typical human gesture. Using multiple sensors that detect contact forces and displacements, we characterized the movements that occurred during handshakes. A typical human-to- human handshake took around 3.63 s (SD = 0.45 s) to perform. It can be divided into three phases: reaching (M = 0.92 s, SD = 0.45 s), contact (M = 1.96 s, SD = 0.46 s), and return (M = 0.75 s, SD = 0.12 s). The handshake was further investigated to understand its subtle movements. Using a multiphase jerk minimization model, a smooth human-to- human handshake can be modelled with fifth or fourth degree polynomials at the reaching and return phases, and a sinusoidal function with exponential decay at the contact phase. We show that the contact phase (1.96 s) can be further divided according to the following subphases: preshake (0.06 s), main shake (1.31 s), postshake (0.06 s), and a period of no movement (0.52 s) just before both hands are retracted. We compared these to the existing handshake models that were proposed for physical human-robot interaction (pHRI). From our findings in human-to-human handshakes, we proposed guidelines for a more natural handshake movement between humanoid robots and their human partners. [ABSTRACT FROM AUTHOR]

The intention to act influences the computations of various task-relevant features. However, little is known about the time course of these computations. Furthermore, it is commonly held that these computations are governed by conjunctive neural representations of the features. But, support for this view comes from paradigms arbitrarily combining task features and affordances, thus requiring representations in working memory. Therefore, the present study used electroencephalography and a well-rehearsed task with features that afford minimal working memory representations to investigate the temporal evolution of feature representations and their potential integration in the brain. Female and male human participants grasped objects or touched them with a knuckle. Objects had different shapes and were made of heavy or light materials with shape and weight being relevant for grasping, not for “knuckling.” Using multivariate analysis showed that representations of object shape were similar for grasping and knuckling. However, only for grasping did early shape representations reactivate at later phases of grasp planning, suggesting that sensorimotor control signals feed back to the early visual cortex. Grasp-specific representations of material/weight only arose during grasp execution after object contact during the load phase. A trend for integrated representations of shape and material also became graspspecific but only briefly during the movement onset. These results suggest that the brain generates action-specific representations of relevant features as required for the different subcomponents of its action computations. Our results argue against the view that goaldirected actions inevitably join all features of a task into a sustained and unified neural representation. [ABSTRACT FROM AUTHOR]

The hindlimbs of Accipitriformes are vital for capturing prey. Research on hindlimb musculature has primarily focused on species preferring avian and mammalian prey, whereas piscivorous species have received limited attention. This study aims to investigate the quantitative characteristics of hindlimb muscles in two piscivorous Accipitriformes, Pandion haliaetus and Haliaeetus albicilla, to discern potential muscular features associated with their specific food preference. The mass and proportion of all hindlimb muscles in both species were assessed based on their primary function (flexion or extension). A Kruskal‐Wallis test was employed to analyze possible differences in muscle mass between species. The allometric relationships between the muscles and body mass were explored with the reduced major axis method. Additionally, a study on the architectural parameters of the primary gripping muscles in P. haliaetus was conducted, using published information from other raptorial birds for comparison. The isometric scaling relationship predominated in the majority of individual muscles helping maintain a proportional relationship relative to body mass. Both species exhibited a similar pattern in terms of quantitative muscle features, implying a preservation of muscle characteristics linked to their predatory capabilities. The largest proportion of hindlimb muscle mass was dedicated to digit flexion in accordance with the grasping abilities of birds of prey. The muscles tibialis cranialis, flexor digitorum longus and flexor hallucis longus exhibited the greatest mass, high physiological cross‐sectional area values, and long fibre, categorizing them as high‐power specialists. These muscles are crucial in the flexion movements that produce the gripping action that characterizes birds of prey. Although no statistically significant differences were detected, each species displayed slightly distinct muscular characteristics, particularly in the architectural properties of the flexor muscles controlling digits II, III, and IV. These variations seem to be associated with differences in their prey preferences. [ABSTRACT FROM AUTHOR]

How humans coordinate digit forces to perform dexterous manipulation is not well understood. This gap is due to the use of tasks devoid of dexterity requirements and/or the use of analytical techniques that cannot isolate the roles that digit forces play in preventing object slip and controlling object position and orientation (pose). In our recent work, we used a dexterous manipulation task and decomposed digit forces into FG, the internal force that prevents object slip, and FM, the force responsible for object pose control. Unlike FG, FM was modulated from object lift onset to hold, suggesting their different sensitivity to sensory feedback acquired during object lift. However, the extent to which FG and FM can be controlled independently remains to be determined. Importantly, how FG and FM change as a function of object property is mathematically indeterminate and therefore requires active modulation. To address this gap, we systematically changed either object mass or external torque. The FM normal component responsible for object orientation control was modulated to changes in object torque but not mass. In contrast, FG was distinctly modulated to changes in object mass and torque. These findings point to a differential sensitivity of FG and FM to task requirements and provide novel insights into the neural control of dexterous manipulation. Importantly, our results indicate that the proposed digit force decomposition has the potential to capture important differences in how sensory inputs are processed and integrated to simultaneously ensure grasp stability and dexterous object pose control. NEW & NOTEWORTHY: Successful dexterous object manipulation requires simultaneous prevention of object slip and object pose control. How these two task goals are attained can be investigated by decomposing digit forces into grasp and manipulation forces, respectively. We found that these forces were characterized by differential sensitivity to changes in object properties (mass and torque). This finding suggests the involvement of distinct sensorimotor mechanisms that, combined, simultaneously ensure grasp stability and dexterous control of object pose. [ABSTRACT FROM AUTHOR]

The rapid development of wearable technologies is increasing research interest in on-body robotics, where relocatable robots can serve as haptic interfaces, support healthcare measurements, or assist with daily activities. However, on-body mobile robotics poses challenges in aspects such as stable locomotion and control. This article proposes a novel small robot design for moving on human limbs that consists of an open grasping mechanism with a spring linkage, where one side holds a pivoting differential drive base (PDDB) with two spherical rollers, and the other side holds an actuated roller for grasping and stabilization. The spherical rollers maintain contact at three points on the limb, optimizing stability with a minimal number of rollers and integrating DC motors within. The PDDB wheels (spherical rollers) enable directional changes on limb surfaces. The combination of the open mechanism, the PDDB, and the spherical rollers allows adaptability to diameter variations along the limb. Furthermore, the mechanism can be easily put on or removed at any point along the limb, eliminating the need to slip the robot over the hand or foot. The kinematic model for the proposed mechanism has been developed. A cascade control strategy is proposed with an outer loop for stable grasping and an inner loop for trajectory adjustments using PDDB roller velocities. An on-limb robot prototype has been built to test its applicability to human arms. Simulation and experimental results validate the design. [ABSTRACT FROM AUTHOR]

With the rise of deep reinforcement learning (RL) methods, many complex robotic manipulation tasks are being solved. However, harnessing the full power of deep learning requires large datasets. Online RL does not suit itself readily into this paradigm due to costly and time-consuming agent-environment interaction. Therefore, many offline RL algorithms have recently been proposed to learn robotic tasks. But mainly, all such methods focus on a single-task or multitask learning, which requires retraining whenever we need to learn a new task. Continuously learning tasks without forgetting previous knowledge combined with the power of offline deep RL would allow us to scale the number of tasks by adding them one after another. This paper investigates the effectiveness of regularisation-based methods like synaptic intelligence for sequentially learning image-based robotic manipulation tasks in an offline-RL setup. We evaluate the performance of this combined framework against common challenges of sequential learning: catastrophic forgetting and forward knowledge transfer. We performed experiments with different task combinations to analyse the effect of task ordering. We also investigated the effect of the number of object configurations and the density of robot trajectories. We found that learning tasks sequentially helps in the retention of knowledge from previous tasks, thereby reducing the time required to learn a new task. Regularisation-based approaches for continuous learning, like the synaptic intelligence method, help mitigate catastrophic forgetting but have shown only limited transfer of knowledge from previous tasks. [ABSTRACT FROM AUTHOR]

Although it is known that the availability of visual feedback modulates grasping kinematics, it is unclear whether this extends to both the early and late stages of the movement. We tackled this issue by exposing participants to the Uznadze illusion (a medium stimulus appears larger or smaller after exposure to smaller or larger inducers). After seeing smaller or larger discs, participants grasped a medium disc with (closed-loop [CL]) or without (open-loop [OL]) visual feedback. Our main aim was to assess whether the time course of the illusion from the movement onset up to the grasp differed between OL and CL. Moreover, we compared OL and CL illusory effects on maximum grip aperture (MGA) and tested whether preparation time, movement time, and time to MGA predicted illusion magnitude. Results revealed that CL illusory effects decreased over movement time, whereas OL ones remained constant. At the time of MGA, OL, and CL effects were, however, of similar size. Although OL grasps were longer to prepare and showed earlier and larger MGAs, such differences had little impact on modulating the illusion. These results suggest that the early stage of grasping is sensitive to the Uznadze illusion both under CL and OL conditions, whereas the late phase is sensitive to it only under OL conditions. We discuss these findings within the framework of theoretical models on the functional properties of the dorsal stream for visually guided actions. [ABSTRACT FROM AUTHOR]

IntroductionWhen humans grasp an object, they are capable of recognizing its characteristics, such as its stiffness and shape, through the sensation of their hands. They can also determine their level of confidence in the estimated object properties. In this study, we developed a method for multi-fingered hands to estimate both physical and geometric properties, such as the stiffness and shape of an object. Their confidence levels were measured using proprioceptive signals, such as joint angles and velocity.MethodWe have developed a learning framework based on probabilistic inference that does not necessitate hyperparameters to maintain equilibrium between the estimation of diverse types of properties. Using this framework, we have implemented recurrent neural networks that estimate the stiffness and shape of grasped objects with their uncertainty in real time.ResultsWe demonstrated that the trained neural networks are capable of representing the confidence level of estimation that includes the degree of uncertainty and task difficulty in the form of variance and entropy.DiscussionWe believe that this approach will contribute to reliable state estimation. Our approach would also be able to combine with flexible object manipulation and probabilistic inference-based decision making.

Robot control in complex and unpredictable scenarios presents challenges such as adaptability, robustness, and human-robot interaction. These scenarios often require robots to perform tasks that involve unknown objects in unstructured environments with high levels of uncertainty. Traditional control methods, such as automatic control, may not be suitable due to their limited adaptability and reliance on prior knowledge. Human-in-the-loop method faces issues such as insufficient feedback, increased failure rates due to noise and delays, and lack of operator immersion, preventing the achievement of human-level performance. This study proposed a shared control framework to achieve a trade-off between efficiency and adaptability by combing the advantages of both teleoperation and automatic control method. The proposed approach combines the advantages of both human and automatic control methods to achieve a balance between performance and adaptability. We developed a linear model to compare three control methods and analyzed the impact of position noise and communication delays on performance. The real-world implementation of the shared control system demonstrates its effectiveness in object grasping and manipulation tasks. The results suggest that shared control can significantly improve grasping efficiency while maintaining adaptability in task execution for practical robotics applications.

The editorial in Frontiers in Neurorobotics discusses the development and challenges of assistive and service robots for health and home applications. The article highlights the importance of human-robot interaction, localization, grasping abilities, and social capabilities in robotic systems. Various studies presented in the editorial address issues such as robot localization, human pose estimation, and the integration of components for advanced robotic systems to support users in healthcare and home environments. The editorial emphasizes the need for ongoing research in areas such as accurate localization, semantic information integration, and empirical testing with real users to improve the functionality and acceptance of assistive robots. [Extracted from the article]

The efficient coordination of fingertip forces to maintain static equilibrium while grasping an object continues to intrigue scientists. While many studies have explored this coordination, most of these studies assumed that interactions of hands primarily occur with rigid inanimate objects. Instead, our daily interactions with living and nonliving entities involve many dynamic, compliant, or fragile bodies. This paper investigates the fingertip force coordination on a manipulandum that changes its shape while grasping it. We designed a five-finger perturbation system with linear actuators at positions corresponding to each finger that would protrude outward from the center of the handle or retract toward the center of the handle as programmed. The behavior of the perturbed fingers and the other fingers while grasping this device was studied. Based on previous experiments on expanding and contracting handles, we hypothesized that each finger would exhibit a comparable response to similar horizontal perturbations. However, the response of the little finger was significantly different from the other fingers. We speculate that the central nervous system demonstrates preferential recruitment of some fingers over others while performing a task. [ABSTRACT FROM AUTHOR]

Abstract Detecting contact when fingers are approaching an object and estimating the magnitude of the force the fingers are exerting on the object after contact are important tasks for a multi-fingered robotic hand to stably grasp objects. However, for a linkage-based under-actuated robotic hand with a self-locking mechanism to realize stable grasping without using external sensors, such tasks are difficult to perform when only analyzing the robot model or only applying data-driven methods. Therefore, in this paper, a hybrid of previous approaches is used to find a solution for realizing stable grasping with an under-actuated hand. First, data from the internal sensors of a robotic hand are collected during its operation. Subsequently, using the robot model to analyze the collected data, the differences between the model and real data are explained. From the analysis, novel data-driven-based algorithms, which can overcome noted challenges to detect contact between a fingertip and the object and estimate the fingertip forces in real-time, are introduced. The proposed methods are finally used in a stable grasp controller to control a triple-fingered under-actuated robotic hand to perform stable grasping. The results of the experiments are analyzed to show that the proposed algorithms work well for this task and can be further developed to be used for other future dexterous manipulation tasks.

Several datasets have been proposed in the literature, focusing on object detection and pose estimation. The majority of them are interested in recognizing isolated objects or the pose of objects in well-organized scenarios. This work introduces a novel dataset that aims to stress vision algorithms in the difficult task of object detection and pose estimation in highly cluttered scenes concerning the specific case of bin picking for the Cluttered Environment Picking Benchmark (CEPB). The dataset provides about 1.5M virtually generated photo-realistic images (RGB + depth + normals + segmentation) of 50K annotated cluttered scenes mixing rigid, soft, and deformable objects of varying sizes used in existing robotic picking benchmarks together with their 3D models (40 objects). Such images include three different camera positions, three light conditions, and multiple High Dynamic Range Imaging (HDRI) maps for domain randomization purposes. The annotations contain the 2D and 3D bounding boxes of the involved objects, the centroids' poses (translation + quaternion), and the visibility percentage of the objects' surfaces. Nearly 10K separated object images are presented to perform simple tests and compare them with more complex cluttered scenarios tests. A baseline performed with the DOPE neural network is reported to highlight the challenges introduced by the novel dataset. [ABSTRACT FROM AUTHOR]

AbstractThis article presents a comprehensive Virtual Reality (VR) grasping taxonomy, which represents the common grasping patterns employed by users in VR, and is directly comparable to real object grasping taxonomies. With grasping being one of the primary interfaces we have with the physical world, seminal work has sought to explore our explicit grasping actions with real objects with the aim to define structured reasoning in the form of taxonomies. However, limited work has replicated this approach, for immersive technology (i.e., VR) and to address this, we present the first complete taxonomy of grasping interaction for VR, which builds on the body of work from real object grasping alongside recent approaches which have been applied into VR. We present a formal elicitation study, wherein a Wizard of Oz (WoZ) methodology is applied with users (N = 50) tasked to grasp and translate virtual twins of real objects. We present results from the analysis of 4800 grasps into a formal structured taxonomy which details the frequency of all potential real grasps and draws comparisons to the body of prior work in both real grasping and VR grasping studies. Results highlight the reduced number of grasp types used in VR (27), and the differences in commonality, frequency, and grasping approach between real grasping and VR grasping taxonomies. Focus is especially given to the nuances of the grasp, and via an in-depth evaluation of object properties, namely shape and size, we illustrate the common trends in VR grasping. Results from this work are also combined with VR grasping findings from prior published work, leading to the presentation of the most common grasps (5) for VR and recommendations for future analysis and use in intuitive and natural VR systems. [ABSTRACT FROM AUTHOR]

The problem of supporting visually impaired and blind people in meaningful interactions with objects is often neglected. To address this issue, we adapted a tactile belt for enhanced spatial navigation into a bracelet worn on the wrist that allows visually impaired people to grasp target objects. Participants' performance in locating and grasping target items when guided using the bracelet, which provides direction commands via vibrotactile signals, was compared to their performance when receiving auditory instructions. While participants were faster with the auditory commands, they also performed well with the bracelet, encouraging future development of this system and similar systems. [ABSTRACT FROM AUTHOR]

As the industry shifts to automated manufacturing and the assembly of parts in smaller batches, there is a clear need for an efficient design of grippers. This paper presents a method for automated grasp planning and finger design for multiple parts using four grasp quality measures that capture the following important requirements for grasping: (i) uniform contact force distribution; (ii) better gravity wrench resistance; (iii) robustness against gripper positioning error; and (iv) ability to resist larger external wrench on the object. We introduce the fingertip score to quantify the grasp performance of a fingertip design over all the objects. The method takes the CAD model of the objects as the input and outputs the optimal grasp location and the best finger design. We use the method for a three-point grasp with a parallel jaw gripper. We validate our method on two sets of objects. Results show how each grasp quality measure behaves on different objects and the variation in the fingertip score with finger design. Finally, we test the effectiveness of the optimal finger design experimentally. The three-point grasp is suitable for grasping objects larger than is possible with shape-matching fingertips. [ABSTRACT FROM AUTHOR]

Modular self-reconfigurable robots hold the promise of being capable of performing a wide variety of tasks. However, many systems fall short of either delivering this promised functionality due to constraints in system architecture or validating it on functional hardware prototypes. This paper demonstrates the functional capabilities of the Planar Adaptive Robot with Triangular Structure (PARTS) and documents the versatility of this robot system using a holistic approach that combines simulations and hardware demonstrations on a prototype with nine fabricated modules. PARTS is a two-dimensional modular robot consisting of modules with a shape-shifting triangular geometry capable of forming adaptable space-covering structures. Meta-modules and mesh restructuring techniques are presented as methods for achieving topological self-reconfiguration. The feasibility of these methods is demonstrated by applying them on a simulated reconfiguration example of 62 modules. The paper showcases the versatility of PARTS on the hardware prototype using task-specific configurations, including locomotion using a meta-module and a walker configuration, module-module interaction by establishing a bridge between two separated module clusters, and interaction with the environment using a gripper and supporting structure configuration. The results validate the versatility and emphasize the potential of the system's design concept, motivating the transfer of the hardware architecture to the third dimension. [ABSTRACT FROM AUTHOR]

In stroke rehabilitation, simple robotic devices hold the potential to increase the training dosage in group therapies and to enable continued therapy at home after hospital discharge. However, we identified a lack of portable and cost-e [ABSTRACT FROM AUTHOR]

This article aims at presenting a detailed and practical comparison between three designs of robotic soft fingers for industrial grippers. While the soft finger based on the fin ray effect (FRE) shown here has been proposed long ago, few works in the literature have studied its reliance on the presence of the crossbeams or its precision grasp performance compared to its power grasps. Aiming at addressing these gaps, two novel designs are proposed and compared to FRE fingers in this article. First, the three designs are presented. One of these fingers, named PacomeFlex, embeds changeable grasping modes by relying on two sets of kinematic structures with a bistable stopper design. Then, finite element analyses (FEA) are conducted to simulate power and precision grasps of the three fingers followed by the estimation of the grasp forces produced. These FEA are then used to train neural networks capable of predicting these grasp forces. Finally, the grasp strength and pullout resistance of the fingers are experimentally measured, and experimental results are shown to be in good accordance with the FEA and neural network models. As will also be shown, the PacomeFlex finger introduced in this work provides a noticeably higher performance level than all other fingers with respect to typical metrics in soft grasping. [ABSTRACT FROM AUTHOR]

Detecting contact when fingers are approaching an object and estimating the magnitude of the force the fingers are exerting on the object after contact are important tasks for a multi-fingered robotic hand to stably grasp objects. However, for a linkage-based under-actuated robotic hand with a self-locking mechanism to realize stable grasping without using external sensors, such tasks are difficult to perform when only analyzing the robot model or only applying data-driven methods. Therefore, in this paper, a hybrid of previous approaches is used to find a solution for realizing stable grasping with an under-actuated hand. First, data from the internal sensors of a robotic hand are collected during its operation. Subsequently, using the robot model to analyze the collected data, the differences between the model and real data are explained. From the analysis, novel data-driven-based algorithms, which can overcome noted challenges to detect contact between a fingertip and the object and estimate the fingertip forces in real-time, are introduced. The proposed methods are finally used in a stable grasp controller to control a triple-fingered under-actuated robotic hand to perform stable grasping. The results of the experiments are analyzed to show that the proposed algorithms work well for this task and can be further developed to be used for other future dexterous manipulation tasks. [ABSTRACT FROM AUTHOR]

Introduction: The concept of affordance refers to the opportunities for action provided by the environment, often conveyed through visual information. It has been applied to explain visuomotor processing and movement planning. As emotion modulates both visual perception and the motor system, it is reasonable to ask whether emotion can influence affordance judgments. If present, this relationship can have important ontological implications for affordances. Thus, we investigated whether the emotional value of manipulable objects affected the judgment of the appropriate grasping that could be used to interact with them (i.e., their affordance). Methods: Volunteers were instructed to use a numerical scale to report their judgment on how an observed object should be grasped. We compared these judgments across emotional categories of objects (pleasant, unpleasant and neutral), while also considering the expected effect of object size. Results: We found that unpleasant objects were rated as more appropriately graspable by a precision grip than pleasant and neutral objects. Simultaneously, smaller object size also favored this judgment. This effect was seen in all emotional categories examined in equal magnitude. Discussion: Our findings suggest that the emotional value of objects modulates affordance judgments in a way that favors careful manipulation and minimal physical contact with aversive stimuli. Finally, we discuss how this affective aspect of our experience of objects overlaps with what affordances are conceptualized to be, calling for further reexamination of the relationship between affordances and emotions. [ABSTRACT FROM AUTHOR]

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Objectives: This study quantifies hallucal metatarsal (Mt1) and second metatarsal (Mt2) polar second moment of areas (J) in hominoids and cercopithecoids to address three questions. Which catarrhines have more rigid Mt1s versus Mt2s? Is asymmetry in medial metatarsal rigidity (e.g., Mt1 > Mt2) different among taxa? Does asymmetry in medial metatarsal rigidity correlate with relative intrinsic hallucal muscle mass across species. Materials and Methods: J was calculated from CT images in 345 specimens and compared between groups: Pan, Gorilla, Pongo, hylobatids, cercopithecines, and colobines. Differences between the Mt1 and Mt2 within groups were assessed using paired t tests. Log10‐transformed Mt1:Mt2 J ratios were used to assess asymmetry in rigidity and inter‐group comparisons were made. Additionally, correlations were performed between species means of log10‐transformed Mt1:Mt2 J ratios and species means of relative intrinsic hallucal muscle mass. Results: African ape and hylobatid Mt1s are significantly more rigid than their Mt2s. Rigidity in medial metatarsals are more variable in Pongo, but most individuals have stronger Mt2s. Greatest variation is observed in cercopithecoids, with some species having consistently stronger Mt1s or Mt2s, but the majority show no bias for either. Log10‐transformed Mt1:Mt2 J ratios are significantly correlated with relative intrinsic hallucal muscle mass; taxa with more rigid Mt1s have more developed muscle. Discussion: Differences between metatarsals in rigidity do not correlate with previously documented regional differences in peak plantar pressure during weight support. Rather, asymmetry in metatarsal rigidity, specifically Mt1 > Mt2, reflect adaptations of larger intrinsic hallucal musculature capable of producing greater hallucal grasping force. [ABSTRACT FROM AUTHOR]

It remains unclear whether accurate motor performance and cortical activation differ among grasping forms across several force levels. In the present study, a ballistic target force matching task (20%, 40%, 60%, and 80% of maximum voluntary force) with power grip, side pinch, and pulp pinch was utilized to explore the accuracy of the forces generated as well as the muscular activity of intrinsic and extrinsic hand muscles. By using near-infrared spectroscopy, we also examined bilateral dorsolateral prefrontal cortex (DLPFC) activation during the preparatory phase (initial 10 s) of the task. The accuracy of the power grip and pulp pinch was relatively higher than that of the side pinch, and the electromyographic activity of intrinsic hand muscles exhibited a similar trend for power grip and side pinch, while the opposite muscle recruitment pattern was observed for pulp pinch. The increment of DLPFC oxygenation across force levels differed among grasping forms, with greater activity at relatively higher levels in the power grip and side pinch, and at relatively lower levels in the pulp pinch. Taken together, the differential contribution of the DLPFC may be responsible for force generation depending on different grasping forms and force levels. [ABSTRACT FROM AUTHOR]

This paper presents the design, development, and testing of a robot that combines soft-body grasping and crawling locomotion to navigate tubular objects. Inspired by the natural snakes' climbing locomotion of tubular objects, the soft robot includes proximal and distal modules with radial expansion/contraction for grasping around the objects and a longitudinal contractile–expandable driving module in-between for providing a bi-directional crawling movement along the length of the object. The robot's grasping modules are made of fabrics, and the crawling module is made of an extensible pneumatic soft actuator (ePSA). Conceptual designs and CAD models of the robot parts, textile-based inflatable structures, and pneumatic driving mechanisms were developed. The mechanical parts were fabricated using advanced and conventional manufacturing techniques. An Arduino-based electro-pneumatic control board was developed for generating cyclic patterns of grasping and locomotion. Different reinforcing patterns and materials characterize the locomotor actuators' dynamical responses to the varying input pressures. The robot was tested in a laboratory setting to navigate a cable, and the collected data were used to modify the designs and control software and hardware. The capability of the soft robot for navigating cables in vertical, horizontal, and curved path scenarios was successfully demonstrated. Compared to the initial design, the forward speed is improved three-fold. [ABSTRACT FROM AUTHOR]

Most robotic hands have been created at roughly the scale of the human hand, with rigid components forming the core structural elements of the fingers. This focus on the human hand has concentrated attention on operations within the human hand scale, and on the handling of objects suitable for grasping with current robot hands. In this paper, we describe the design, development, and testing of a four-fingered gripper which features a novel combination of actively actuated rigid and compliant elements. The scale of the gripper is unusually large compared to most existing robot hands. The overall goal for the hand is to explore compliant grasping of potentially fragile objects of a size not typically considered. The arrangement of the digits is inspired by the feet of birds, specifically raptors. We detail the motivation for this physical hand structure, its design and operation, and describe testing conducted to assess its capabilities. The results demonstrate the effectiveness of the hand in grasping delicate objects of relatively large size and highlight some limitations of the underlying rigid/compliant hybrid design. [ABSTRACT FROM AUTHOR]

The end-state comfort effect (ESC) describes the tendency to grasp an object with an initial uncomfortable grasp posture in order to achieve a comfortable end posture. The ESC is an example for anticipative processes in manual action. ESC planning is investigated in many studies where this effect is measured in the context of motor observation and motion capture. However, there is little evidence if the anticipative link between different action states, especially between initial grasp postures and comfortable end postures, is represented in memory. The aim of the present study was to investigate whether the perception of a grasp posture holding a bar leads to the activation of action-related representations of grasping actions. For this purpose, a priming paradigm was used in which prime images were shown depicting either a comfortable (overhand grip) or uncomfortable (underhand grip) grasp posture holding a two-colored bar. The subsequently shown target images represented either a comfortable (thumb-up) or uncomfortable (thumb-down) final grasp posture of this grasping action. Due to the different grasp postures in the prime and target, prime-target pairs represented different types of action sequences. Furthermore, physically possible, and physically impossible actions were presented. Participants were asked to react to the top color of the bar shown in the target-picture, whereby the shown grasp posture was irrelevant for this decision. Results showed that reaction times did not differ after presentation of an overhand grip to target pictures showing comfortable or uncomfortable final grasp postures. In contrast, after presentation of an underhand grip in the prime, reactions to target pictures with final comfortable grasp postures were faster compared to target pictures with uncomfortable grasp postures. The effect was only found for the physically possible action. The findings suggest that the perception of the underhand grip leads to cognitive pre-activation of a final action state. The present study suggests that the association between an initial uncomfortable underhand grip and its action effect, in form of a final action state that is consistent with the ESC, is represented in memory. Such motor representation might be important for the anticipation and control of goal-directed grasping.

Tactile sensors play a pivotal role in enhancing robotic gripping systems across various industries, allowing robots to interact with their environment with precision and adaptability. This study introduces a novel soft barometric tactile sensor designed to capture pressure data beneath the contact pad within an elastomer layer. By employing an iterative fitting process for a parameterized pressure distribution, the sensor eliminates the need for extensive calibration or complex training procedures. A novel metric was introduced to evaluate the accuracy of pressure reconstruction in tactile sensors. This was complemented by quantitative investigations. Moreover, the precision of multiple contact detection and the capabilities of force estimation were examined. Finally, the practical applications of the sensor were demonstrated through in-hand measurement of object orientation. The sensor exhibits promising results, accurately reconstructing pressure distributions for a wide range of indenters, reliably discerning two contacts when their distance exceeds 5 mm and estimating forces with a Mean Absolute Error (MAE) of 1.83 N. Additionally, the in-hand reorientation achieved a Mean Absolute Rotation Error (MARE) of 6.81°, demonstrating the sensor’s potential for real-world applications.

The demand for drone-based object transportation has been rapidly growing recently. Current drones face limitations in their ability to grasp objects on the flat surface. To address this issue, our study presents a universal jamming holding system with the capability to grasp flat surfaces. In general, jamming grippers cannot grasp flat surfaces. Here we have introduced a stretchable membrane and spherical shape to the gripper, allowing our jamming gripper to grasp flat surfaces. We assumed that when the stretchable membrane attaches to an object, the stretchable membrane spontaneously forms microscopic suction cup-like structure at the contact area. Our jamming gripper then can grasp flat objects by the effect of the suction cup-like structure. Our jamming gripper is ideal for drone mounting as it maintains the center of gravity during the gripping process and can operate with low positional accuracy. In addition, our jamming gripper operate on a pneumatic circuit powered by a solenoid valve, allowing our drone to hold objects without consuming energy. Our experimental results demonstrated that an acrylic plate could support a load of 945 g on a flat surface at a filling rate of 75%. Our gripper also has a good adsorption force on metal, acrylic, ice, and other surfaces. The gripping force of our grippers is independent of the pressing force. Our jamming gripper can be mounted on a drone and controlled wirelessly to perform grasping movements autonomously, making them highly versatile. It can be applied to various situations where the shapes of objects to be grasped are uncertain, such as delivery of goods to remote areas or sample collection.

Currently, multi-posture robots have complex grasping robotic manipulators with low power density, making it difficult to miniaturize and integrate. In this paper, a multi-posture grasping manipulator actuated by shape memory alloy with different functional modules is presented. It is composed of deflection, translation, rotation and grasping modules. Based on a D-H parameter method, the end motion trajectory model is established and the end motion space is drawn. Finally, the grasping experiment of a light circular object is carried out to verify the validity of the multi-posture grasping function of the multi-module combination manipulator, which provides a choice for future intelligent robot manipulators.

With the advent of robotics and artificial intelligence, the potential for automating tasks within human-centric environments has increased significantly. This is particularly relevant in the retail sector where the demand for efficient operations and the shortage of labor drive the need for rapid advancements in robot-based technologies. Densely packed retail shelves pose unique challenges for robotic manipulation and detection due to limited space and diverse object shapes. Vacuum-based grasping technologies offer a promising solution but face challenges with object shape adaptability. The study proposes a framework for robotic grasping in retail environments, an adaptive vacuum-based grasping solution, and a new evaluation metric—termed grasp shear force resilience—for measuring the effectiveness and stability of the grasp during manipulation. The metric provides insights into how retail objects behave under different manipulation scenarios, allowing for better assessment and optimization of robotic grasping performance. The study’s findings demonstrate the adaptive suction cups’ ability to successfully handle a wide range of object shapes and sizes, which, in some cases, overcome commercially available solutions, particularly in adaptability. Additionally, the grasp shear force resilience metric highlights the effects of the manipulation process, such as in shear force and shake, on the manipulated object. This offers insights into its interaction with different vacuum cup grasping solutions in retail picking and restocking scenarios.

The study introduces a suction cup-based soft robotic gripper for cucumber harvesting that adjusts its shape and surface parameters to respond to the surface and shape characteristics of cucumbers. The gripper shape is optimised for adsorption to the large curvature of the cucumber, increasing the effective radius related to the grasping force. The suction cup surface is also modified to maintain adsorption on uneven cucumber surfaces. The test of the suction cup verifies the validity of these critical parameters and demonstrates that the proposed gripper produces robust adsorption, increasing the adsorption success rate and effective radius. The gripper integrated with the cutting module was verified at three sites with varying cucumber characteristics. The test of the end-effector was conducted 174 times, and the success rate was 86.2%, with a damage rate of 4.7%. The success rate was lowest at Site A (76.8%) and highest at Site C (95.2%), which is attributed to the thickness and length of the cucumber stem. The gripper function is independent of the cutting module, and its analysis motivates the future improvement of the cutting module. The proposed gripper can contribute to effective cucumber harvesting when employed with the improved cutting module. [Display omitted] • Soft and compact robotic gripper for efficient harvesting in dense agriculture • Shape and surface adjustment enables effective grasping of bumpy cucumbers. • Suction cup shape adjustment improves grasping force despite size reduction. • Suction cup surface adjustment for robustness to torsion on cucumber surfaces. • The suction cup validation was performed in a lab and three different fields. [ABSTRACT FROM AUTHOR]

A defining feature of most primates is a hand with five fingers. Spider monkeys are an exception because they have four fingers and no thumb. Despite the prevalence of reach-to-grasp research in primates, it is not known how the lack of a thumb affects reaching and grasping in spider monkeys. Drawing on patterns that have been well described in human adults, human infants, and other nonhuman primates, this study characterized prehension in Colombian spider monkeys (Ateles fusciceps rufiventris). Monkeys reached for two differently sized food objects and reaches were digitized offline for two-dimensional kinematic analysis. Grasp strategy was coded from video as preshaped when the hand was adjusted to grasp the food before contact, or not preshaped when the hand was adjusted to grasp the food after contact. Monkeys exhibited variability in reach smoothness that contrasted with the typical pattern seen in other adult primates and instead resembled the pattern observed in human infants. Monkeys anticipated the object to be grasped approximately half of the time. Reaches where the hand was preshaped to the object were smoother than reaches where the hand was adjusted to grasp after object contact. For the small object, reaches with preshaping were straighter than reaches without preshaping. Results are the first evidence of kinematic signatures for reach-to-grasp actions in spider monkeys. [ABSTRACT FROM AUTHOR]