Replicating human hand synergies onto robotic hands: a review on software and hardware strategies

This review reports the principal solutions proposed in the literature to reduce the complexity of the control and of the design of robotic hands taking inspiration from the organization of the human brain. Several studies in neuroscience concerning the sensorimotor organization of the human hand proved that, despite the complexity of the hand, a few parameters can describe most of the variance in the patterns of configurations and movements. In other words, humans exploit a reduced set of parameters, known in the literature as synergies, to control their hands. In robotics, this dimensionality reduction can be achieved by coupling some of the degrees of freedom (DoFs) of the robotic hand, that results in a reduction of the needed inputs. Such coupling can be obtained at the software level, exploiting mapping algorithm to reproduce human hand organization, and at the hardware level, through either rigid or compliant physical couplings between the joints of the robotic hand. This paper reviews the main solutions proposed for both the approaches

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Synergy-Based Human Grasp Representations and Semi-Autonomous Control of Prosthetic Hands

Das sichere und stabile Greifen mit humanoiden Roboterhänden stellt eine große Herausforderung dar. Diese Dissertation befasst sich daher mit der Ableitung von Greifstrategien für Roboterhände aus der Beobachtung menschlichen Greifens. Dabei liegt der Fokus auf der Betrachtung des gesamten Greifvorgangs. Dieser umfasst zum einen die Hand- und Fingertrajektorien während des Greifprozesses und zum anderen die Kontaktpunkte sowie den Kraftverlauf zwischen Hand und Objekt vom ersten Kontakt bis zum statisch stabilen Griff. Es werden nichtlineare posturale Synergien und Kraftsynergien menschlicher Griffe vorgestellt, die die Generierung menschenähnlicher Griffposen und Griffkräfte erlauben. Weiterhin werden Synergieprimitive als adaptierbare Repräsentation menschlicher Greifbewegungen entwickelt. Die beschriebenen, vom Menschen gelernten Greifstrategien werden für die Steuerung robotischer Prothesenhände angewendet. Im Rahmen einer semi-autonomen Steuerung werden menschenähnliche Greifbewegungen situationsgerecht vorgeschlagen und vom Nutzenden der Prothese überwacht

The role of morphology of the thumb in anthropomorphic grasping : a review

The unique musculoskeletal structure of the human hand brings in wider dexterous capabilities to grasp and manipulate a repertoire of objects than the non-human primates. It has been widely accepted that the orientation and the position of the thumb plays an important role in this characteristic behavior. There have been numerous attempts to develop anthropomorphic robotic hands with varying levels of success. Nevertheless, manipulation ability in those hands is to be ameliorated even though they can grasp objects successfully. An appropriate model of the thumb is important to manipulate the objects against the fingers and to maintain the stability. Modeling these complex interactions about the mechanical axes of the joints and how to incorporate these joints in robotic thumbs is a challenging task. This article presents a review of the biomechanics of the human thumb and the robotic thumb designs to identify opportunities for future anthropomorphic robotic hands

Tensegrity and Recurrent Neural Networks: Towards an Ecological Model of Postural Coordination

Tensegrity systems have been proposed as both the medium of haptic perception and the functional architecture of motor coordination in animals. However, a full working model integrating those two aspects with some form of neural implementation is still lacking. A basic two-dimensional cross-tensegrity plant is designed and its mechanics simulated. The plant is coupled to a Recurrent Neural Network (RNN). The model’s task is to maintain postural balance against gravity despite the intrinsically unstable configuration of the plant. The RNN takes only proprioceptive input about the springs’ lengths and rate of length change and outputs minimum lengths for each spring which modulates their interaction with the plant’s inertial kinetics. Four artificial agents are evolved to coordinate the patterns of spring contractions in order to maintain dynamic equilibrium. A first study assesses quiet standing performance and reveals coordinative patterns between the tensegrity rods akin to humans’ strategy of anti-phase hip-ankle relative phase. The agents show a mixture of periodic and aperiodic trajectories of their Center of Mass. Moreover, the agents seem to tune to the anticipatory “time-to-balance” quantity in order to maintain their movements within a region of reversibility. A second study perturbs the systems with mechanical platform shifts and sensorimotor degradation. The agents’ response to the mechanical perturbation is robust. Dimensionality analysis of the RNNs’ unit activations reveals a pattern of degree of freedom recruitment after perturbation. In the degradation sub-study, different levels of noise are added to the RNN inputs and different levels of weakening gain are applied to the forces generated by the springs to mimic haptic degradation and muscular weakening in elderly humans. As expected, the systems perform less well, falling earlier than without the insults. However, the same systems re-evolved again under the degraded conditions see significant functional recovery. Overall, the dissertation supports the plausibility of RNN cum tensegrity models of haptics-guided postural coordination in humans

A distributive approach to tactile sensing for application to human movement

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis thesis investigates on clinical applicability of a novel sensing technology in the areas of postural steadiness and stroke assessment. The mechanically simple Distributive Tactile Sensing approach is applied to extract motion information from flexible surfaces to identify parameters and disorders of human movement in real time. The thesis reports on the design, implementation and testing of smart platform devices which are developed for discrimination applications through the use of linear and non-linear data interpretation techniques and neural networks for pattern recognition. In the thesis mathematical models of elastic plates, based on finite element and finite difference methods, are developed and described. The models are used to identify constructive parameters of sensing devices by investigating sensitivity and accuracy of Distributive Tactile Sensing surfaces. Two experimental devices have been constructed for the investigation. These are a sensing floor platform for standing applications and a sensing chair for sitting applications. Using a linear approach, the sensing floor platform is developed to detect centre of pressure, an important parameter widely used in the assessment of postural steadiness. It is demonstrated that the locus of centre of pressure can be determined with an average deviation of 1.05mm from that of a commercialised force platform in a balance application test conducted with five healthy volunteers. This amounts to 0.4% of the sensor range. The sensing chair used neural networks for pattern recognition, to identify the level of motor impairment in people with stroke through performing functional reaching task while sitting. The clinical studies with six real stroke survivors have shown the robustness of the sensing technique to deal with a range of possible motion in the reaching task investigated. The work of this thesis demonstrates that the novel Distributive Tactile Sensing approach is suited to clinical and home applications as screening and rehabilitation systems. Mechanical simplicity is a merit of the approach and has potential to lead to versatile low-cost units

Robotic manipulator inspired by human fingers based on tendon-driven soft grasping

Die menschliche Hand ist in der Lage, verschiedene Greif- und Manipulationsaufgaben auszuführen und kann als einer der geschicktesten und vielseitigsten Effektoren angesehen werden. In dieser Arbeit wurde ein Soft Robotic-Greifer entwickelt, der auf den Erkenntnissen aus der Literatur zur Taxonomie der menschlichen Greiffähigkeiten und den biomechanischen Synergien der menschlichen Hand basiert. Im Bereich der Roboterhände sind sehnengetriebene, unteraktuierte Strukturen weit verbreitet. Inspiriert von der Anatomie der menschlichen Hand, bieten sie durch ihre Flexibilität passive Adaptivität und Robustheit. Es wurde ein Sensorsystem implementiert, bestehend aus Force Sensing Resistors (FSRs), Biegungssensoren und einem Stromsensor, wodurch das System charakterisiert werden kann. Die Kraftsensoren wurden in die Fingerkuppen integriert. In Anlehnung an die menschliche Haut wurden Abgüsse aus Silikonkautschuk an den Fingerballen verwendet. Diese versprechen eine erhöhte Reibung und bessere Adaptivität zum gegriffenen Objekt. Um den entwickelten Greifer zu evaluieren, wurden erste Tests durchgeführt. Zunächst wurde die Funktionalität der Sensoren, wie z.B. der als FSRs ausgewählten Kraftsensoren, getestet. Im weiteren Verlauf wurden die Greiffähigkeiten des Greifers durch Manipulation verschiedener Objekte getestet. Basierend auf den Erkenntnissen aus den praktischen Versuchen kann festgestellt werden, dass der entwickelte Greifer ein hohes Maß an Geschicklichkeit aufweist. Auch die Adaptivität ist dank der verwendeten mechanischen Komponenten gewährleistet. Mittels der Sensorik ist es möglich, den Greifprozess zu kontrollieren. Die Ergebnisse zeigen aber auch, dass z. B. die interne Systemreibung die Verlustleistung des Systems stark beeinflusst.The human hand is able to perform various grasping and manipulation tasks, and can be seen as one of the most dexterous and versatile effectors known. The prehensile capabilities of the hand have already been analyzed, categorized and summarized in a taxonomy in numerous studies. In addition to the taxonomies, research on the biomechanical synergies of the human hand led to the following conceptions: The adduction/abduction movement is independent of the flexion/extension movement. Furthermore, the thumb is rather independent in its mobility from the other fingers, while those move synchronously within their corresponding joints. Lastly, the consideration of the synergies provides that the proximal and distal interphalangeal joints of a human finger are more intensely coordinated than those of the metacarpal joints. In this work, a soft robotic gripper was developed based on the knowledge from the literature on the taxonomy of human gripping abilities and the biomechanical synergies of the human hand. In the domain of robotic hands, tendon-driven underactuated structures are widely used. Inspired by the tensegrity structure of the human hand, they offer passive adaptivity and robustness through their flexibility. A sensor system was implemented, consisting of FSRs, flex sensors and a current sensor, thus the system parameters can be characterized continously. The force sensors were integrated into the fingertips. Molds of silicone rubber were used as finger pads to provide higher friction and better adaptivity to the grasped object on the contact areas of the finger, to mimic human skin. Initial tests were carried out to evaluate the gripper. First, the functionality of the sensors, such as the force sensors selected as FSRs, was tested. In the further course, the gripping capabilities of the gripper were tested by manipulation of various different objects. Based on the findings from the practical experiments, it may be stated that the gripper has a high degree of dexterity. Thanks to the mechanical components used, adaptivity is guaranteed as well. By means of the sensor system it is possible to control the gripping processes. However, the results also showed that, for example, the internal system friction dominates the system’s power dissipation

A SYSTEMS APPROACH TO THE PROBLEM OF FALLS IN OLD AGE

The problem of falls in old age is enormously costly and disruptive for the older individual, others, and society, and its severity is likely to intensify as our population ages. This dissertation takes a systems-oriented approach toward the falls problem and is presented in two parts. The first part critically develops a new approach to the problem of falls. The second part describes an empirical study that applies this new approach in a pragmatic manner. Conventional fall prevention strategies employ a reductionist approach to the problem of falls. This approach is questioned because it corresponds poorly to the holistic nature of postural control. A systems-oriented conceptual framework explains postural instability in old age as the gradual decline of a postural control system’s ability to adapt. Realizing that falls arise from a complex system of interacting components of various levels and domains makes it imperative to investigate interventions aimed toward systemically fostering robust postural control. A dynamic systems theoretical framework is outlined that views postural control to be the result of synergies which function to control myriad inherent degrees of freedom. Complexity-based measures of postural sway are suggested as indicators of postural control system robustness. This new approach to the problem of falls is applied in an empirical study in which Tai Chi serves as a systems-oriented intervention. Using a dynamic systems perspective, motor imagery, along with other Tai Chi principles, are hypothesized to provide interacting physical and cognitive constraints on motor behavior that form synergies which enable robust postural stability into old age. This hypothesis was tested in a quasi-experiment comparing effects of Tai Chi motor imagery in Tai Chi experts and non-experts. The expected significant effects on postural sway complexity were not found, but significant main effects and interactions on sway variability and ease of imagery were discovered with respect to expertise and imagery type. Findings, results, innovations, implications and future directions are presented, and discussed as they pertain to four specific aims, and to ameliorating the problem of falls in old age

Muscleless Motor synergies and actions without movements : From Motor neuroscience to cognitive robotics

Emerging trends in neurosciences are providing converging evidence that cortical networks in predominantly motor areas are activated in several contexts related to ‘action’ that do not cause any overt movement. Indeed for any complex body, human or embodied robot inhabiting unstructured environments, the dual processes of shaping motor output during action execution and providing the self with information related to feasibility, consequence and understanding of potential actions (of oneself/others) must seamlessly alternate during goal-oriented behaviors, social interactions. While prominent approaches like Optimal Control, Active Inference converge on the role of forward models, they diverge on the underlying computational basis. In this context, revisiting older ideas from motor control like the Equilibrium Point Hypothesis and synergy formation, this article offers an alternative perspective emphasizing the functional role of a ‘plastic, configurable’ internal representation of the body (body-schema) as a critical link enabling the seamless continuum between motor control and imagery. With the central proposition that both “real and imagined” actions are consequences of an internal simulation process achieved though passive goal-oriented animation of the body schema, the computational/neural basis of muscleless motor synergies (and ensuing simulated actions without movements) is explored. The rationale behind this perspective is articulated in the context of several interdisciplinary studies in motor neurosciences (for example, intracranial depth recordings from the parietal cortex, FMRI studies highlighting a shared cortical basis for action ‘execution, imagination and understanding’), animal cognition (in particular, tool-use and neuro-rehabilitation experiments, revealing how coordinated tools are incorporated as an extension to the body schema) and pertinent challenges towards building cognitive robots that can seamlessly “act, interact, anticipate and understand” in unstructured natural living spaces