The role of haptic feedback when manipulating nonrigid objects

Enhancing stroke rehabilitation with whole-hand haptic rendering: development and clinical usability evaluation of a novel upper-limb rehabilitation device

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.

Human manipulation strategy when changing object deformability and task properties

Robotic literature widely addresses deformable object manipulation, but few studies analyzed human manipulation accounting for different levels of deformability and task properties. We asked participants to grasp and insert rigid and deformable objects into holes with varying tolerances and depths, and we analyzed the grasping behavior, the reaching velocity profile, and completion times. Results indicated that the more deformable the object is, the nearer the grasping point is to the extremity to be inserted. For insertions in the long hole, the selection of the grasping point is a trade-off between task accuracy and the number of re-grasps required to complete the insertion. The compliance of the deformable object facilitates the alignment between the object and the hole. The reaching velocity profile when increasing deformability recalls the one observed when task accuracy and precision decrease. Identifying human strategy allows the implementation of human-inspired high-level reasoning algorithms for robotic manipulation.

Towards functional robotic training: motor learning of dynamic tasks is enhanced by haptic rendering but hampered by arm weight support

Background

Current robot-aided training allows for high-intensity training but might hamper the transfer of learned skills to real daily tasks. Many of these tasks, e.g., carrying a cup of coffee, require manipulating objects with complex dynamics. Thus, the absence of somatosensory information regarding the interaction with virtual objects during robot-aided training might be limiting the potential benefits of robotic training on motor (re)learning. We hypothesize that providing somatosensory information through the haptic rendering of virtual environments might enhance motor learning and skill transfer. Furthermore, the inclusion of haptic rendering might increase the task realism, enhancing participants’ agency and motivation. Providing arm weight support during training might also enhance learning by limiting participants’ fatigue.

Methods

We conducted a study with 40 healthy participants to evaluate how haptic rendering and arm weight support affect motor learning and skill transfer of a dynamic task. The task consisted of inverting a virtual pendulum whose dynamics were haptically rendered on an exoskeleton robot designed for upper limb neurorehabilitation. Participants trained with or without haptic rendering and with or without weight support. Participants’ task performance, movement strategy, effort, motivation, and agency were evaluated during baseline, short- and long-term retention. We also evaluated if the skills acquired during training transferred to a similar task with a shorter pendulum.

Results

We found that haptic rendering significantly increases participants’ movement variability during training and the ability to synchronize their movements with the pendulum, which is correlated with better performance. Weight support also enhances participants’ movement variability during training and reduces participants’ physical effort. Importantly, we found that training with haptic rendering enhances motor learning and skill transfer, while training with weight support hampers learning compared to training without weight support. We did not observe any significant differences between training modalities regarding agency and motivation during training and retention tests.

Conclusion

Haptic rendering is a promising tool to boost robot-aided motor learning and skill transfer to tasks with similar dynamics. However, further work is needed to find how to simultaneously provide robotic assistance and haptic rendering without hampering motor learning, especially in brain-injured patients.

Trial registrationhttps://clinicaltrials.gov/show/NCT04759976

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-00993-w.

A Novel Clinical-Driven Design for Robotic Hand Rehabilitation: Combining Sensory Training, Effortless Setup, and Large Range of Motion in a Palmar Device

Neurorehabilitation research suggests that not only high training intensity, but also somatosensory information plays a fundamental role in the recovery of stroke patients. Yet, there is currently a lack of easy-to-use robotic solutions for sensorimotor hand rehabilitation. We addressed this shortcoming by developing a novel clinical-driven robotic hand rehabilitation device, which is capable of fine haptic rendering, and that supports physiological full flexion/extension of the fingers while offering an effortless setup. Our palmar design, based on a parallelogram coupled to a principal revolute joint, introduces the following novelties: (1) While allowing for an effortless installation of the user's hand, it offers large range of motion of the fingers (full extension to 180° flexion). (2) The kinematic design ensures that all fingers are supported through the full range of motion and that the little finger does not lose contact with the finger support in extension. (3) We took into consideration that a handle is usually comfortably grasped such that its longitudinal axis runs obliquely from the metacarpophalangeal joint of the index finger to the base of the hypothenar eminence. (4) The fingertip path was optimized to guarantee physiologically correct finger movements for a large variety of hand sizes. Moreover, the device possesses a high mechanical transparency, which was achieved using a backdrivable cable transmission. The transparency was further improved with the implementation of friction and gravity compensation. In a test with six healthy participants, the root mean square of the human-robot interaction force was found to remain as low as 1.37 N in a dynamic task. With its clinical-driven design and easy-to-use setup, our robotic device for hand sensorimotor rehabilitation has the potential for high clinical acceptance, applicability and effectiveness.

Preparing to move: Setting initial conditions to simplify interactions with complex objects

Humans dexterously interact with a variety of objects, including those with complex internal dynamics. Even in the simple action of carrying a cup of coffee, the hand not only applies a force to the cup, but also indirectly to the liquid, which elicits complex reaction forces back on the hand. Due to underactuation and nonlinearity, the object's dynamic response to an action sensitively depends on its initial state and can display unpredictable, even chaotic behavior. With the overarching hypothesis that subjects strive for predictable object-hand interactions, this study examined how subjects explored and prepared the dynamics of an object for subsequent execution of the target task. We specifically hypothesized that subjects find initial conditions that shorten the transients prior to reaching a stable and predictable steady state. Reaching a predictable steady state is desirable as it may reduce the need for online error corrections and facilitate feed forward control. Alternative hypotheses were that subjects seek to reduce effort, increase smoothness, and reduce risk of failure. Motivated by the task of 'carrying a cup of coffee', a simplified cup-and-ball model was implemented in a virtual environment. Human subjects interacted with this virtual object via a robotic manipulandum that provided force feedback. Subjects were encouraged to first explore and prepare the cup-and-ball before initiating a rhythmic movement at a specified frequency between two targets without losing the ball. Consistent with the hypotheses, subjects increased the predictability of interaction forces between hand and object and converged to a set of initial conditions followed by significantly decreased transients. The three alternative hypotheses were not supported. Surprisingly, the subjects' strategy was more effortful and less smooth, unlike the observed behavior in simple reaching movements. Inverse dynamics of the cup-and-ball system and forward simulations with an impedance controller successfully described subjects' behavior. The initial conditions chosen by the subjects in the experiment matched those that produced the most predictable interactions in simulation. These results present first support for the hypothesis that humans prepare the object to minimize transients and increase stability and, overall, the predictability of hand-object interactions.

Motor memories of object dynamics are categorically organized

The ability to predict the dynamics of objects, linking applied force to motion, underlies our capacity to perform many of the tasks we carry out on a daily basis. Thus, a fundamental question is how the dynamics of the myriad objects we interact with are organized in memory. Using a custom-built three-dimensional robotic interface that allowed us to simulate objects of varying appearance and weight, we examined how participants learned the weights of sets of objects that they repeatedly lifted. We find strong support for the novel hypothesis that motor memories of object dynamics are organized categorically, in terms of families, based on covariation in their visual and mechanical properties. A striking prediction of this hypothesis, supported by our findings and not predicted by standard associative map models, is that outlier objects with weights that deviate from the family-predicted weight will never be learned despite causing repeated lifting errors.

Composition and decomposition of visuomotor maps during manual tracking

Adapting hand movements to changes in our body or the environment is essential for skilled motor behavior, as is the ability to flexibly combine experience gathered in separate contexts. However, it has been shown that when adapting hand movements to two different visuomotor perturbations in succession, interference effects can occur. Here, we investigate whether these interference effects compromise our ability to adapt to the superposition of the two perturbations. Participants tracked with a joystick, a visual target that followed a smooth but an unpredictable trajectory. Four separate groups of participants (total n = 83) completed one block of 50 trials under each of three mappings: one in which the cursor was rotated by 90° (ROTATION), one in which the cursor mimicked the behavior of a mass-spring system (SPRING), and one in which the SPRING and ROTATION mappings were superimposed (SPROT). The order of the blocks differed across groups. Although interference effects were found when switching between SPRING and ROTATION, participants who performed these blocks first performed better in SPROT than participants who had no prior experience with SPRING and ROTATION (i.e., composition). Moreover, participants who started with SPROT exhibited better performance under SPRING and ROTATION than participants who had no prior experience with each of these mappings (i.e., decomposition). Additional analyses confirmed that these effects resulted from components of learning that were specific to the rotational and spring perturbations. These results show that interference effects do not preclude the ability to compose/decompose various forms of visuomotor adaptation.NEW & NOTEWORTHY The ability to compose/decompose task representations is critical for both cognitive and behavioral flexibility. Here, we show that this ability extends to two forms of visuomotor adaptation in which humans have to perform visually guided hand movements. Despite the presence of interference effects when switching between visuomotor maps, we show that participants are able to flexibly compose or decompose knowledge acquired in previous sessions. These results further demonstrate the flexibility of sensorimotor adaptation in humans.

Promoting Motor Variability During Robotic Assistance Enhances Motor Learning of Dynamic Tasks

Despite recent advances in robot-assisted training, the benefits of haptic guidance on motor (re)learning are still limited. While haptic guidance may increase task performance during training, it may also decrease participants' effort and interfere with the perception of the environment dynamics, hindering somatosensory information crucial for motor learning. Importantly, haptic guidance limits motor variability, a factor considered essential for learning. We propose that Model Predictive Controllers (MPC) might be good alternatives to haptic guidance since they minimize the assisting forces and promote motor variability during training. We conducted a study with 40 healthy participants to investigate the effectiveness of MPCs on learning a dynamic task. The task consisted of swinging a virtual pendulum to hit incoming targets with the pendulum ball. The environment was haptically rendered using a Delta robot. We designed two MPCs: the first MPC-end-effector MPC-applied the optimal assisting forces on the end-effector. A second MPC-ball MPC-applied its forces on the virtual pendulum ball to further reduce the assisting forces. The participants' performance during training and learning at short- and long-term retention tests were compared to a control group who trained without assistance, and a group that trained with conventional haptic guidance. We hypothesized that the end-effector MPC would promote motor variability and minimize the assisting forces during training, and thus, promote learning. Moreover, we hypothesized that the ball MPC would enhance the performance and motivation during training but limit the motor variability and sense of agency (i.e., the feeling of having control over their movements), and therefore, limit learning. We found that the MPCs reduce the assisting forces compared to haptic guidance. Training with the end-effector MPC increases the movement variability and does not hinder the pendulum swing variability during training, ultimately enhancing the learning of the task dynamics compared to the other groups. Finally, we observed that increases in the sense of agency seemed to be associated with learning when training with the end-effector MPC. In conclusion, training with MPCs enhances motor learning of tasks with complex dynamics and are promising strategies to improve robotic training outcomes in neurological patients.

Analysis of subject specific grasping patterns

Existing haptic feedback devices are limited in their capabilities and are often cumbersome and heavy. In addition, these devices are generic and do not adapt to the users’ grasping behavior. Potentially, a human-oriented design process could generate an improved design. While current research done on human grasping was aimed at finding common properties within the research population, we investigated the dynamic patterns that make human grasping behavior distinct rather than generalized, i.e. subject specific. Experiments were conducted on 31 subjects who performed grasping tasks on five different objects. The kinematics and kinetics parameters were measured using a motion capture system and force sensors. The collected data was processed through a pipeline of dimensionality reduction and clustering algorithms. Using finger joint angles and reaction forces as our features, we were able to classify these tasks with over 95% success. In addition, we examined the effects of the objects’ mechanical properties on those patterns and the significance of the different features for the differentiation. Our results suggest that grasping patterns are, indeed, subject-specific; this, in turn, could suggest that a device capable of providing personalized feedback can improve the user experience and, in turn, increase the usability in different applications. This paper explores an undiscussed aspect of human dynamic patterns. Furthermore, the collected data offer a valuable dataset of human grasping behavior, containing 1083 grasp instances with both kinetics and kinematics data.

Gaze behavior during visuomotor tracking with complex hand-cursor dynamics

The ability to track a moving target with the hand has been extensively studied, but few studies have characterized gaze behavior during this task. Here we investigate gaze behavior when participants learn a new mapping between hand and cursor motion, such that the cursor represented the position of a virtual mass attached to the grasped handle via a virtual spring. Depending on the experimental condition, haptic feedback consistent with mass-spring dynamics could also be provided. For comparison a simple one-to-one hand-cursor mapping was also tested. We hypothesized that gaze would be drawn, at times, to the cursor in the mass-spring conditions, especially in the absence of haptic feedback. As expected hand tracking performance was less accurate under the spring mapping, but gaze behavior was virtually unaffected by the spring mapping, regardless of whether haptic feedback was provided. Specifically, relative gaze position between target and cursor, rate of saccades, and gain of smooth pursuit were similar under both mappings and both haptic feedback conditions. We conclude that even when participants are exposed to a challenging hand-cursor mapping, gaze is primarily concerned about ongoing target motion suggesting that peripheral vision is sufficient to monitor cursor position and to update hand movement control.

Motion planning strategies in human control of non-rigid objects with internal degrees of freedom

The paper deals with modeling of human-like reaching movements in dynamic environments. A simple but not trivial example of reaching in a dynamic environment is the rest-to-rest manipulation of a multi-mass flexible object with the elimination of residual vibrations. Two approaches to the prediction of reaching movements are formulated in position and force actuation settings. In the first approach, either the position of the hand or the hand force is specified by the lowest order polynomial satisfying the boundary conditions of the reaching task. The second approach is based on the minimization of either the hand jerk or the hand force-change, with taking into account the dynamics of the flexible object. To verify the resulting four mathematical models, an experiment on the manipulation of a ten-masses flexible object of low stiffness is conducted. The experimental results show that the second approach gives a significantly better prediction of human movements, with the minimum hand force-change model having a slight but consistent edge over the minimum hand jerk one.

Asymmetrical Relationship between Prediction and Control during Visuomotor Adaptation

Current theories suggest that the ability to control the body and to predict its associated sensory consequences is key for skilled motor behavior. It is also suggested that these abilities need to be updated when the mapping between motor commands and sensory consequences is altered. Here we challenge this view by investigating the transfer of adaptation to rotated visual feedback between one task in which human participants had to control a cursor with their hand in order to track a moving target, and another in which they had to predict with their eyes the visual consequences of their hand movement on the cursor. Hand and eye tracking performances were evaluated respectively through cursor–target and eye–cursor distance. Results reveal a striking dissociation: although prior adaptation of hand tracking greatly facilitates eye tracking, the adaptation of eye tracking does not transfer to hand tracking. We conclude that although the update of control is associated with the update of prediction, prediction can be updated independently of control. To account for this pattern of results, we propose that task demands mediate the update of prediction and control. Although a joint update of prediction and control seemed mandatory for success in our hand tracking task, the update of control was only facultative for success in our eye tracking task. More generally, those results promote the view that prediction and control are mediated by separate neural processes and suggest that people can learn to predict movement consequences without necessarily promoting their ability to control these movements.

Predictability, force, and (anti)resonance in complex object control

Manipulation of complex objects as in tool use is ubiquitous and has given humans an evolutionary advantage. This study examined the strategies humans choose when manipulating an object with underactuated internal dynamics, such as a cup of coffee. The dynamics of the object renders the temporal evolution complex, possibly even chaotic, and difficult to predict. A cart-and-pendulum model, loosely mimicking coffee sloshing in a cup, was implemented in a virtual environment with a haptic interface. Participants rhythmically manipulated the virtual cup containing a rolling ball; they could choose the oscillation frequency, whereas the amplitude was prescribed. Three hypotheses were tested: 1) humans decrease interaction forces between hand and object; 2) humans increase the predictability of the object dynamics; and 3) humans exploit the resonances of the coupled object-hand system. Analysis revealed that humans chose either a high-frequency strategy with antiphase cup-and-ball movements or a low-frequency strategy with in-phase cup-and-ball movements. Counter to hypothesis 1, they did not decrease interaction force; instead, they increased the predictability of the interaction dynamics, quantified by mutual information, supporting hypothesis 2. To address hypothesis 3, frequency analysis of the coupled hand-object system revealed two resonance frequencies separated by an antiresonance frequency. The low-frequency strategy exploited one resonance, whereas the high-frequency strategy afforded more choice, consistent with the frequency response of the coupled system; both strategies avoided the antiresonance. Hence, humans did not prioritize small interaction forces but rather strategies that rendered interactions predictable. These findings highlight that physical interactions with complex objects pose control challenges not present in unconstrained movements. NEW & NOTEWORTHY Daily actions involve manipulation of complex nonrigid objects, which present a challenge since humans have no direct control of the whole object. We used a virtual-reality experiment and simulations of a cart-and-pendulum system coupled to hand movements with impedance to analyze the manipulation of this underactuated object. We showed that participants developed strategies that increased the predictability of the object behavior by exploiting the resonance structure of the object but did not minimize the hand-object interaction force.

Eye Tracking of Occluded Self-Moved Targets: Role of Haptic Feedback and Hand-Target Dynamics

Previous studies on smooth pursuit eye movements have shown that humans can continue to track the position of their hand, or a target controlled by the hand, after it is occluded, thereby demonstrating that arm motor commands contribute to the prediction of target motion driving pursuit eye movements. Here, we investigated this predictive mechanism by manipulating both the complexity of the hand-target mapping and the provision of haptic feedback. Two hand-target mappings were used, either a rigid (simple) one in which hand and target motion matched perfectly or a nonrigid (complex) one in which the target behaved as a mass attached to the hand by means of a spring. Target animation was obtained by asking participants to oscillate a lightweight robotic device that provided (or not) haptic feedback consistent with the target dynamics. Results showed that as long as 7 s after target occlusion, smooth pursuit continued to be the main contributor to total eye displacement (∼60%). However, the accuracy of eye-tracking varied substantially across experimental conditions. In general, eye-tracking was less accurate under the nonrigid mapping, as reflected by higher positional and velocity errors. Interestingly, haptic feedback helped to reduce the detrimental effects of target occlusion when participants used the nonrigid mapping, but not when they used the rigid one. Overall, we conclude that the ability to maintain smooth pursuit in the absence of visual information can extend to complex hand-target mappings, but the provision of haptic feedback is critical for the maintenance of accurate eye-tracking performance.

Eye tracking a self-moved target with complex hand-target dynamics

Previous work has shown that the ability to track with the eye a moving target is substantially improved when the target is self-moved by the subject's hand compared with when being externally moved. Here, we explored a situation in which the mapping between hand movement and target motion was perturbed by simulating an elastic relationship between the hand and target. Our objective was to determine whether the predictive mechanisms driving eye-hand coordination could be updated to accommodate this complex hand-target dynamics. To fully appreciate the behavioral effects of this perturbation, we compared eye tracking performance when self-moving a target with a rigid mapping (simple) and a spring mapping as well as when the subject tracked target trajectories that he/she had previously generated when using the rigid or spring mapping. Concerning the rigid mapping, our results confirmed that smooth pursuit was more accurate when the target was self-moved than externally moved. In contrast, with the spring mapping, eye tracking had initially similar low spatial accuracy (though shorter temporal lag) in the self versus externally moved conditions. However, within ∼5 min of practice, smooth pursuit improved in the self-moved spring condition, up to a level similar to the self-moved rigid condition. Subsequently, when the mapping unexpectedly switched from spring to rigid, the eye initially followed the expected target trajectory and not the real one, thereby suggesting that subjects used an internal representation of the new hand-target dynamics. Overall, these results emphasize the stunning adaptability of smooth pursuit when self-maneuvering objects with complex dynamics.

Movement Strategy Discovery during Training via Haptic Guidance

Haptic guidance has previously been investigated to facilitate motor skill training, whereupon a robotic device assists a trainee in executing the desired movement. However, many studies have reported a null or even detrimental effect of haptic guidance on training compared to unassisted practice. While prior studies have focused on using haptic guidance to refine a movement strategy, our study explores its effect on the discovery of a new strategy. Subjects learned to manipulate a virtual under-actuated system via a haptic device either with or without haptic guidance (and without haptic feedback of system dynamics). The guidance enabled subjects to experience a range of successful movements, rather than strictly enforcing one trajectory. Subjects who trained with guidance adopted a strategy that involved faster reaches, required greater control of the system's degrees of freedom, and increased the potential for faster task completion. However, overall improvement of task performance was limited with the new strategy.

Predictability and Robustness in the Manipulation of Dynamically Complex Objects

Manipulation of complex objects and tools is a hallmark of many activities of daily living, but how the human neuromotor control system interacts with such objects is not well understood. Even the seemingly simple task of transporting a cup of coffee without spilling creates complex interaction forces that humans need to compensate for. Predicting the behavior of an underactuated object with nonlinear fluid dynamics based on an internal model appears daunting. Hence, this research tests the hypothesis that humans learn strategies that make interactions predictable and robust to inaccuracies in neural representations of object dynamics. The task of moving a cup of coffee is modeled with a cart-and-pendulum system that is rendered in a virtual environment, where subjects interact with a virtual cup with a rolling ball inside using a robotic manipulandum. To gain insight into human control strategies, we operationalize predictability and robustness to permit quantitative theory-based assessment. Predictability is quantified by the mutual information between the applied force and the object dynamics; robustness is quantified by the energy margin away from failure. Three studies are reviewed that show how with practice subjects develop movement strategies that are predictable and robust. Alternative criteria, common for free movement, such as maximization of smoothness and minimization of force, do not account for the observed data. As manual dexterity is compromised in many individuals with neurological disorders, the experimental paradigm and its analyses are a promising platform to gain insights into neurological diseases, such as dystonia and multiple sclerosis, as well as healthy aging.

Rapid Visuomotor Corrective Responses during Transport of Hand-Held Objects Incorporate Novel Object Dynamics

Numerous studies have shown that people are adept at learning novel object dynamics, linking applied force and motion, when performing reaching movements with hand-held objects. Here we investigated whether the control of rapid corrective arm responses, elicited in response to visual perturbations, has access to such newly acquired knowledge of object dynamics. Participants first learned to make reaching movements while grasping an object subjected to complex load forces that depended on the distance and angle of the hand from the start position. During a subsequent test phase, we examined grip and load force coordination during corrective arm movements elicited (within ∼150 ms) in response to viewed sudden lateral shifts (1.5 cm) in target or object position. We hypothesized that, if knowledge of object dynamics is incorporated in the control of the corrective responses, grip force changes would anticipate the unusual load force changes associated with the corrective arm movements so as to support grasp stability. Indeed, we found that the participants generated grip force adjustments tightly coupled, both spatially and temporally, to the load force changes associated with the arm movement corrections. We submit that recently learned novel object dynamics are effectively integrated into sensorimotor control policies that support rapid visually driven arm corrective actions during transport of hand held objects. Significance statement: Previous studies have demonstrated that the motor system can learn, and make use of, internal models of object dynamics to generate feedforward motor commands. However, it is not known whether such internal models are incorporated into rapid, automatic arm movement corrections that compensate for errors that arise during movement. Here we demonstrate, for the first time, that internal models of novel object dynamics are integrated into rapid corrective arm movements made in response to visuomotor perturbations that, importantly, do not directly perturb the object.

Inertial torque during reaching directly impacts grip-force adaptation to weightless objects

A hallmark of movement control expressed by healthy humans is the ability to gradually improve motor performance through learning. In the context of object manipulation, previous work has shown that the presence of a torque load has a direct impact on grip-force control, characterized by a significantly slower grip-force adjustment across lifting movements. The origin of this slower adaptation rate remains unclear. On the one hand, information about tangential constraints during stationary holding may be difficult to extract in the presence of a torque. On the other hand, inertial torque experienced during movement may also potentially disrupt the grip-force adjustments, as the dynamical constraints clearly differ from the situation when no torque load is present. To address the influence of inertial torque loads, we instructed healthy adults to perform visually guided reaching movements in weightlessness while holding an unbalanced object relative to the grip axis. Weightlessness offered the possibility to remove gravitational constraints and isolate the effect of movement-related feedback on grip force adjustments. Grip-force adaptation rates were compared with a control group who manipulated a balanced object without any torque load and also in weightlessness. Our results clearly show that grip-force adaptation in the presence of a torque load is significantly slower, which suggests that the presence of torque loads experienced during movement may alter our internal estimates of how much force is required to hold an unbalanced object stable. This observation may explain why grasping objects around the expected location of the center of mass is such an important component of planning and control of manipulation tasks.

The effect of force feedback delay on stiffness perception and grip force modulation during tool-mediated interaction with elastic force fields

During interaction with objects, we form an internal representation of their mechanical properties. This representation is used for perception and for guiding actions, such as in precision grip, where grip force is modulated with the predicted load forces. In this study, we explored the relationship between grip force adjustment and perception of stiffness during interaction with linear elastic force fields. In a forced-choice paradigm, participants probed pairs of virtual force fields while grasping a force sensor that was attached to a haptic device. For each pair, they were asked which field had higher level of stiffness. In half of the pairs, the force feedback of one of the fields was delayed. Participants underestimated the stiffness of the delayed field relatively to the nondelayed, but their grip force characteristics were similar in both conditions. We analyzed the magnitude of the grip force and the lag between the grip force and the load force in the exploratory probing movements within each trial. Right before answering which force field had higher level of stiffness, both magnitude and lag were similar between delayed and nondelayed force fields. These results suggest that an accurate internal representation of environment stiffness and time delay was used for adjusting the grip force. However, this representation did not help in eliminating the bias in stiffness perception. We argue that during performance of a perceptual task that is based on proprioceptive feedback, separate neural mechanisms are responsible for perception and action-related computations in the brain.

Grip force control during virtual object interaction: effect of force feedback,accuracy demands, and training

When grasping and manipulating objects, people are able to efficiently modulate their grip force according to the experienced load force. Effective grip force control involves providing enough grip force to prevent the object from slipping, while avoiding excessive force to avoid damage and fatigue. During indirect object manipulation via teleoperation systems or in virtual environments, users often receive limited somatosensory feedback about objects with which they interact. This study examines the effects of force feedback, accuracy demands, and training on grip force control during object interaction in a virtual environment. The task required subjects to grasp and move a virtual object while tracking a target. When force feedback was not provided, subjects failed to couple grip and load force, a capability fundamental to direct object interaction. Subjects also exerted larger grip force without force feedback and when accuracy demands of the tracking task were high. In addition, the presence or absence of force feedback during training affected subsequent performance, even when the feedback condition was switched. Subjects' grip force control remained reminiscent of their employed grip during the initial training. These results motivate the use of force feedback during telemanipulation and highlight the effect of force feedback during training.

Separate contributions of kinematic and kinetic errors to trajectory and grip force adaptation when transporting novel hand-held loads

Numerous studies of motor learning have examined the adaptation of hand trajectories and grip forces when moving grasped objects with novel dynamics. Such objects initially result in both kinematic and kinetic errors; i.e., mismatches between predicted and actual trajectories and between predicted and actual load forces. Here we investigated the contribution of these errors to both trajectory and grip force adaptation. Participants grasped an object with novel dynamics using a precision grip and moved it between two targets. Kinematic errors could be effectively removed using a force channel to constrain hand motion to a straight line. When moving in the channel, participants learned to modulate grip force in synchrony with load force and this learning generalized when movement speed in the channel was doubled. When the channel was removed, these participants continued to effectively modulate grip force but exhibited substantial kinematic errors, equivalent to those seen in participants who did not previously experience the object in the channel. We also found that the rate of grip force adaptation did not depend on whether the object was initially moved with or without a channel. These results indicate that kinematic errors are necessary for trajectory but not grip force adaptation, and that kinetic errors are sufficient for grip force but not trajectory adaptation. Thus, participants can learn a component of the object's dynamics, used to control grip force, based solely on kinetic errors. However, this knowledge is apparently not accessible or usable for controlling the movement trajectory when the channel is removed.