Stability control of grasping objects with different locations of center of mass and rotational inertia

Dexterous manipulation: differential sensitivity of manipulation and grasp forces to task requirements

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.

A two-stage grasp detection method for sequential robotic grasping in stacking scenarios

Dexterous grasping is essential for the fine manipulation tasks of intelligent robots; however, its application in stacking scenarios remains a challenge. In this study, we aimed to propose a two-phase approach for grasp detection of sequential robotic grasping, specifically for application in stacking scenarios. In the initial phase, a rotated-YOLOv3 (R-YOLOv3) model was designed to efficiently detect the category and position of the top-layer object, facilitating the detection of stacked objects. Subsequently, a stacked scenario dataset with only the top-level objects annotated was built for training and testing the R-YOLOv3 network. In the next phase, a G-ResNet50 model was developed to enhance grasping accuracy by finding the most suitable pose for grasping the uppermost object in various stacking scenarios. Ultimately, a robot was directed to successfully execute the task of sequentially grasping the stacked objects. The proposed methodology demonstrated the average grasping prediction success rate of 96.60% as observed in the Cornell grasping dataset. The results of the 280 real-world grasping experiments, conducted in stacked scenarios, revealed that the robot achieved a maximum grasping success rate of 95.00%, with an average handling grasping success rate of 83.93%. The experimental findings demonstrated the efficacy and competitiveness of the proposed approach in successfully executing grasping tasks within complex multi-object stacked environments.

Brain functional networks analysis of five fingers grasping in virtual reality environment

This study investigated the effects of different center of mass (COM) of the grasping device and visual time-delay on the information interaction between brain regions during five-finger grasping process. Nine healthy right-handed subjects used five fingers to grasp a special device in a virtual reality (VR) environment. Two independent variables were set in the experiment: the COM of the grasping device and the visual delay time. Place a 50 g mass randomly at five different directions of the grasping device base. The three levels of visual delay time appear randomly. The kinematics and dynamics and electroencephalogram (EEG) signals were recorded during the experiment. The brain network was constructed based on multiplex horizontal visibility graph (MHVG). Interlayer mutual information (MI) and phase locking value (PLV) were calculated to quantify the network, while clustering coefficient (C), shortest path length (L) and overall network efficiency (E) are selected to quantify the network characteristic. Statistical results show that when the mass is located in the radial side, during the load phase of grasping, the C and E is significantly higher than that in the proximal, ulnar and medial side, and L was significantly lower than that in the proximal and radial side. This shows that when grasping an object with a COM bias on the radial side, the process of brain feedforward control has higher level of information interaction and ability and it can build stronger sensorimotor memory. It is also found that the brain network features of theta, beta and gamma bands of EEG are positively correlated, especially between beta and gamma bands, which suggests there is a coupling relationship between different bands in information processing and transmission.Clinical Relevance- This study explains the neural mechanism of grasping control from the topological structure of the whole brain network level and the informatics.

Do Tangential Finger Forces Utilize Mechanical Advantage During Moment of Force production?

This study investigated the beneficial effects of the utilization of mechanical advantage (MA) of finger tangential forces during the moment production. Subjects produced the resistive moment of force against the external torque while the moment arms of the tangential forces were systematically changed. We observed a relatively large contribution to the net moment by the tangential forces with the increased moment arms, whereas the vector sum of normal and tangential forces decreased. The indices of multi-finger coordination for the stabilization of the moment of forces and force direction increased with the moment arms. The current results provide evidence that the utilization of MA is associated with both the efficiency of force production and the stabilization of performance variables.

Improving Reliability of Myocontrol Using Formal Verification

In the context of assistive robotics, myocontrol is one of the so-far unsolved problems of upper-limb prosthetics. It consists of swiftly, naturally, and reliably converting biosignals, non-invasively gathered from an upper-limb disabled subject, into control commands for an appropriate self-powered prosthetic device. Despite decades of research, traditional surface electromyography cannot yet detect the subject's intent to an acceptable degree of reliability, that is, enforce an action exactly when the subject wants it to be enforced.. In this paper, we tackle one such kind of mismatch between the subject's intent and the response by the myocontrol system, and show that formal verification can indeed be used to mitigate it. Eighteen intact subjects were engaged in two target achievement control tests in which a standard myocontrol system was compared to two "repaired" ones, one based on a non-formal technique, thus enforcing no guarantee of safety, and the other using the satisfiability modulo theories (SMT) technology to rigorously enforce the desired property. The experimental results indicate that both repaired systems exhibit better reliability than the non-repaired one. The SMT-based system causes only a modest increase in the required computational resources with respect to the non-formal technique; as opposed to this, the non-formal technique can be easily implemented in existing myocontrol systems, potentially increasing their reliability.

Left visual field preference for a bimanual grasping task with ecologically valid object sizes

Grasping using two forelimbs in opposition to one another is evolutionary older than the hand with an opposable thumb (Whishaw and Coles in Behav Brain Res 77:135-148, 1996); yet, the mechanisms for bimanual grasps remain unclear. Similar to unimanual grasping, the localization of matching stable grasp points on an object is computationally expensive and so it makes sense for the signals to converge in a single cortical hemisphere. Indeed, bimanual grasps are faster and more accurate in the left visual field, and are disrupted if there is transcranial stimulation of the right hemisphere (Le and Niemeier in Exp Brain Res 224:263-273, 2013; Le et al. in Cereb Cortex. doi: 10.1093/cercor/bht115, 2013). However, research so far has tested the right hemisphere dominance based on small objects only, which are usually grasped with one hand, whereas bimanual grasping is more commonly used for objects that are too big for a single hand. Because grasping large objects might involve different neural circuits than grasping small objects (Grol et al. in J Neurosci 27:11877-11887, 2007), here we tested whether a left visual field/right hemisphere dominance for bimanual grasping exists with large and thus more ecologically valid objects or whether the right hemisphere dominance is a function of object size. We asked participants to fixate to the left or right of an object and to grasp the object with the index and middle fingers of both hands. Consistent with previous observations, we found that for objects in the left visual field, the maximum grip apertures were scaled closer to the object width and were smaller and less variable, than for objects in the right visual field. Our results demonstrate that bimanual grasping is predominantly controlled by the right hemisphere, even in the context of grasping larger objects.