The Role of Learning and Kinematic Features in Dexterous Manipulation: a Comparative Study with Two Robotic Hands

Dexterous movements performed by the human hand are by far more sophisticated than those achieved by current humanoid robotic hands and systems used to control them. This work aims at providing a contribution in order to overcome this gap by proposing a bio-inspired control architecture that captures two key elements underlying human dexterity. The first is the progressive development of skilful control, often starting from – or involving – cyclic movements, based on trial-and-error learning processes and central pattern generators. The second element is the exploitation of a particular kinematic features of the human hand, i.e. the thumb opposition. The architecture is tested with two simulated robotic hands having different kinematic features and engaged in rotating spheres, cylinders, and cubes of different sizes. The results support the feasibility of the proposed approach and show the potential of the model to allow a better understanding of the control mechanisms and kinematic principles underlying human dexterity and make them transferable to anthropomorphic robotic hands

Differences between kinematic synergies and muscle synergies during two-digit grasping

International audienceThe large number of mechanical degrees of freedom of the hand is not fully exploited during actual movements such as grasping. Usually, angular movements in various joints tend to be coupled, and EMG activities in different hand muscles tend to be correlated. The occurrence of covariation in the former was termed kinematic synergies, in the latter muscle synergies. This study addresses two questions: (i) Whether kinematic and muscle synergies can simultaneously accommodate for kinematic and kinetic constraints. (ii) If so, whether there is an interrelation between kinematic and muscle synergies. We used a reach-grasp-and-pull paradigm and recorded the hand kinematics as well as eight surface EMGs. Subjects had to either perform a precision grip or side grip and had to modify their grip force in order to displace an object against a low or high load. The analysis was subdivided into three epochs: reach, grasp-and-pull, and static hold. Principal component analysis (PCA, temporal or static) was performed separately for all three epochs, in the kinematic and in the EMG domain. PCA revealed that (i) Kinematic-and muscle-synergies can simultaneously accommodate kinematic (grip type) and kinetic task constraints (load condition). (ii) Upcoming grip and load conditions of the grasp are represented in kinematic-and muscle-synergies already during reach. Phase plane plots of the principal muscle-synergy against the principal kinematic synergy revealed (iii) that the muscle-synergy is linked (correlated, and in phase advance) to the kinematic synergy during reach and during grasp-and-pull. Furthermore (iv), pair-wise correlations of EMGs during hold suggest that muscle-synergies are (in part) implemented by coactivation of muscles through common input. Together, these results suggest that kinematic synergies have (at least in part) their origin not just in muscular activation, but in synergistic muscle activation. In short: kinematic synergies may result from muscle synergies

The role of learning and kinematic features in dexterous manipulation: a comparative study with two robotic hands

Dexterous movements performed by the human hand are by far more sophisticated than those achieved by current humanoid robotic hands and systems used to control them. This work aims at providing a contribution in order to overcome this gap by proposing a bio-inspired control architecture that captures two key elements underlying human dexterity. The first is the progressive development of skilful control, often starting from - or involving - cyclic movements, based on trial-and-error learning processes and central pattern generators. The second element is the exploitation of a particular kinematic features of the human hand, i.e. the thumb opposition. The architecture is tested with two simulated robotic hands having different kinematic features and engaged in rotating spheres, cylinders, and cubes of different sizes. The results support the feasibility of the proposed approach and show the potential of the model to allow a better understanding of the control mechanisms and kinematic principles underlying human dexterity and make them transferable to anthropomorphic robotic hands

Multilevel control of an anthropomorphic prosthetic hand for grasp and slip prevention

The success of grasping and manipulation tasks of commercial prosthetic hands is mainly related to amputee visual feedback since they are not provided either with tactile sensors or with sophisticated control. As a consequence, slippage and object falls often occur. This article wants to address the specific issue of enhancing grasping and manipulation capabilities of existing prosthetic hands, by changing the control strategy. For this purpose, it proposes a multilevel control based on two distinct levels consisting of (1) a policy search learning algorithm combined with central pattern generators in the higher level and (2) a parallel force/position control managing slippage events in the lower level. The control has been tested on an anthropomorphic robotic hand with prosthetic features (the IH2 hand) equipped with force sensors. Bi-digital and tri-digital grasping tasks with and without slip information have been carried out. The KUKA-LWR has been employed to perturb the grasp stability inducing controlled slip events. The acquired data demonstrate that the proposed control has the potential to adapt to changes in the environment and guarantees grasp stability, by avoiding object fall thanks to prompt slippage event detection

Classifcazione sEMG simultanea di movimenti di mano/polso e delle forze, Convegno Nazionale CINI sull’Intelligenza Articiale

Il presente studio introduce un sistema di classificazione innovativo con il duplice scopo di riconoscere 7 possibili gesti di mano e polso e di regolare il livello di forza che l’utente desidera applicare nell’interazione con gli oggetti. Per fare ciò è stato sviluppato un sistema di classificazione che consente, tramite l’impiego della teoria della macchina a stati (FSM), la gestione contemporanea di tre classificatori basati sull’algoritmo Non-Linear Logistic Regression (NLR): il primo discrimina le 7 classi di movimento, mentre gli altri due classificano i tre livelli di forza modulati durante l’esecuzione di una presa di potenza ed una presa bidigitale

Hierarchical reinforcement learning and central pattern generators for modeling the development of rhythmic manipulation skills

The development of manipulation skills is a fundamental process for young primates as it leads them to acquire the capacity to modify the world to their advantage. As other motor skills, manipulation develops on the basis of motor babbling processes which are initially heavily based on the production of rhythmic movements. We propose a computational bio-inspired model to investigate the development of functional rhythmic hand skills from initially unstructured movements. The model is based on a hierarchical reinforcement-learning actor-critic model that searches the parameters of a set of central pattern generators (CPGs) having different degrees of sophistication. The model is tested with a simulated robotic hand engaged in rotating bottle cap-like objects having different shape and size. The results show that the model is capable of developing skills based on different combinations of CPGs so as to suitably manipulate the different objects. Overall, the model shows to be a valuable tool for the study of the development of rhythmic manipulation skills in primates

Sensorimotor integration within the primary motor cortex by selective nerve fascicle stimulation

The integration of sensory inputs in the motor cortex is crucial for dexterous movement. We recently demonstrated that a closed-loop control based on the feedback provided through intraneural multi-channel electrodes implanted in the median and ulnar nerves of a participant with upper limb amputation improved manipulation skills and increased prosthesis embodiment. Here we assessed, in the same participant, whether and how selective intraneural sensory stimulation also elicits a measurable cortical activation and affects sensorimotor cortical circuits. After estimating the activation of the primary somatosensory cortex evoked by intraneural stimulation, sensorimotor integration was investigated by testing the inhibition of primary motor cortex (M1) output to transcranial magnetic stimulation, after both intraneural and perineural stimulation. Selective sensory intraneural stimulation evoked a low-amplitude,16 ms-latency, parietal response in the same area of the earliest component evoked by whole-nerve stimulation,compatible with fast-conducting afferent fiber activation. For the first time, we show that the same intraneural stimulation was also capable of decreasing M1 output, at the same time range of the short-latency afferent inhibition effect of whole-nerve superficial stimulation. The inhibition generated by the stimulation of channels activating only sensory fibers was stronger than the one due to intraneural or perineural stimulation of channels activating mixed fibers. We demonstrate in a human subject that the cortical sensorimotor integration inhibiting M1 output previously described after the experimental whole-nerve stimulation is present also with a more ecological selective sensory fiber stimulation

Multilevel control of an anthropomorphic prosthetic hand for grasp and slip prevention

The success of grasping and manipulation tasks of commercial prosthetic hands is mainly related to amputee visual feedback since they are not provided either with tactile sensors or with sophisticated control. As a consequence, slippage and object falls often occur. This article wants to address the specific issue of enhancing grasping and manipulation capabilities of existing prosthetic hands, by changing the control strategy. For this purpose, it proposes a multilevel control based on two distinct levels consisting of (1) a policy search learning algorithm combined with central pattern generators in the higher level and (2) a parallel force/position control managing slippage events in the lower level. The control has been tested on an anthropomorphic robotic hand with prosthetic features (the IH2 hand) equipped with force sensors. Bi-digital and tri-digital grasping tasks with and without slip information have been carried out. The KUKA-LWR has been employed to perturb the grasp stability inducing controlled slip events. The acquired data demonstrate that the proposed control has the potential to adapt to changes in the environment and guarantees grasp stability, by avoiding object fall thanks to prompt slippage event detection

Control of prosthetic hands via the peripheral nervous system

This paper intends to provide a critical review of the literature on the technological issues on control and sensorization of hand prostheses interfacing with the Peripheral Nervous System (i.e., PNS), and their experimental validation on amputees. The study opens with an in-depth analysis of control solutions and sensorization features of research and commercially available prosthetic hands. Pros and cons of adopted technologies, signal processing techniques and motion control solutions are investigated. Special emphasis is then dedicated to the recent studies on the restoration of tactile perception in amputees through neural interfaces. The paper finally proposes a number of suggestions for designing the prosthetic system able to re-establish a bidirectional communication with the PNS and foster the prosthesis natural control