The Anthropomorphic Hand Assessment Protocol (AHAP)

The progress in the development of anthropomorphic hands for robotic and prosthetic applications has not been followed by a parallel development of objective methods to evaluate their performance. The need for benchmarking in grasping research has been recognized by the robotics community as an important topic. In this study we present the Anthropomorphic Hand Assessment Protocol (AHAP) to address this need by providing a measure for quantifying the grasping ability of artificial hands and comparing hand designs. To this end, the AHAP uses 25 objects from the publicly available Yale-CMU-Berkeley Object and Model Set thereby enabling replicability. It is composed of 26 postures/tasks involving grasping with the eight most relevant human grasp types and two non-grasping postures. The AHAP allows to quantify the anthropomorphism and functionality of artificial hands through a numerical Grasping Ability Score (GAS). The AHAP was tested with different hands, the first version of the hand of the humanoid robot ARMAR-6 with three different configurations resulting from attachment of pads to fingertips and palm as well as the two versions of the KIT Prosthetic Hand. The benchmark was used to demonstrate the improvements of these hands in aspects like the grasping surface, the grasp force and the finger kinematics. The reliability, consistency and responsiveness of the benchmark have been statistically analyzed, indicating that the AHAP is a powerful tool for evaluating and comparing different artificial hand designs

Anthropomorphism Index of Mobility for Artificial Hands

The increasing development of anthropomorphic artificial hands makes necessary quick metrics that analyze their anthropomorphism. In this study, a human grasp experiment on the most important grasp types was undertaken in order to obtain an Anthropomorphism Index of Mobility (AIM) for artificial hands. The AIM evaluates the topology of the whole hand, joints and degrees of freedom (DoFs), and the possibility to control these DoFs independently. It uses a set of weighting factors, obtained from analysis of human grasping, depending on the relevance of the different groups of DoFs of the hand. The computation of the index is straightforward, making it a useful tool for analyzing new artificial hands in early stages of the design process and for grading human-likeness of existing artificial hands. Thirteen artificial hands, both prosthetic and robotic, were evaluated and compared using the AIM, highlighting the reasons behind their differences. The AIM was also compared with other indexes in the literature with more cumbersome computation, ranking equally different artificial hands. As the index was primarily proposed for prosthetic hands, normally used as nondominant hands in unilateral amputees, the grasp types selected for the human grasp experiment were the most relevant for the human nondominant hand to reinforce bimanual grasping in activities of daily living. However, it was shown that the effect of using the grasping information from the dominant hand is small, indicating that the index is also valid for evaluating the artificial hand as dominant and so being valid for bilateral amputees or robotic hands

Διεπαφές Ηλεκτρομυογραφικών Σημάτων για την Αλληλεπίδραση Ανθρώπου Ρομποτικών Συστημάτων σε Δομημένα και Δυναμικά Περιβάλλοντα

173 σ.Σε αυτή την διδακτορική διατριβή επικεντρωνόμαστε σε διεπαφές ηλεκτρομυογραφικών σημάτων οι οποίες μπορούν να χρησιμοποιηθούν για εφαρμογές αλληλεπίδρασης ανθρώπου ρομποτικών συστημάτων, τόσο σε δομημένα όσο και σε δυναμικά περιβάλλοντα. Αρχικά παρουσιάζουμε μια σειρά από προηγμένα σχήματα μηχανικής μάθησης για διεπαφές ηλεκτρομυογραφικών σημάτων, τα οποία συνδυάζουν έναν ταξινομητή με έναν παλινδρομητή, προκειμένου να κατακερματίσουν τον χώρο δράσης του ρομπότ, προσφέροντας καλύτερα αποτελέσματα αποκωδικοποίησης της ανθρώπινης κίνησης με μοντέλα εκπαιδευμένα για συγκεκριμένες διεργασίες. Όσον αφορά τις εφαρμογές αλληλεπίδρασης ανθρώπου ρομπότ, επικεντρωνόμαστε κυρίως στη έννοια και τις διαφορετικές χρήσεις του ανθρωπομορφισμού των ρομποτικών συστημάτων. Αρχικά διακρίνουμε τις διαφορετικές έννοιες του ανθρωπομορφισμού και εισάγουμε την έννοια του λειτουργικού ανθρωπομορφισμού για σχήματα αντιστοίχησης της ανθρώπινης κίνησης σε ανθρωπομορφική ρομποτική κίνηση, τηρώντας παράλληλα συγκεκριμένους περιορισμούς που θέτει ο χρήστης. Στην συνέχεια προτείνουμε μια ολοκληρωμένη μεθοδολογία για την ποσοτικοποίηση του ανθρωπομορφισμού των ρομποτικών χεριών, βασισμένη σε μεθόδους θεωρίας συνόλων και υπολογιστικής γεωμετρίας. Η συγκεκριμένη μεθοδολογία παρέχει ένα κατανοητό μετρικό του ανθρωπομορφισμού το οποίο κυμαίνεται από 0 (μη-ανθρωπομορφικά ρομποτικά συστήματα) σε 1 (ανθρωπομορφικά ρομποτικά συστήματα) και μπορεί να χρησιμοποιηθεί για διαφορετικά είδη ρομπότ. Τέλος, αναπτύσσουμε μια σειρά από ρομποτικά χέρια, ανοιχτού υλικού και κώδικα, τα οποία είναι ελαφριά, χαμηλού κόστους, εύκολα συναρμολογούμενα, υποϋπενεργούμενα και εγγενώς υποχωρητικά. Τα συγκεκριμένα χέρια μπορούν να χρησιμοποιηθούν τόσο για μελέτες ηλεκτρομυογραφικού ελέγχου (ακόμη και για οικονομικά μυοηλεκτρικά προσθετικά χέρια), όσο και για εφαρμογές αλληλεπίδρασης ανθρώπου ρομποτικών συστημάτων (για μελέτες τηλεχειρισμού ρομποτικών συστημάτων βραχίονα – χεριού), για την αρπαγή πληθώρας καθημερινών αντικειμένων σε δυναμικά περιβάλλοντα (ακόμη και υπό συνθήκες αβεβαιότητας σχετικά με τη θέση και το σχήμα των αντικειμένων). Προκειμένου να αποδείξουμε την αποδοτικότητα και λειτουργικότητα των προτεινόμενων μεθοδολογιών, εκτελέσαμε σειρά πειραμάτων με διαφορετικά ρομποτικά συστήματα, τόσο σε δυναμικά όσο και σε δομημένα περιβάλλοντα.In this PhD thesis we focus on EMG based interfaces that can be efficiently used for Human Robot Interaction (HRI) applications in structured and dynamic environments. Initially, we present a series of advanced learning schemes for EMG based interfaces that take advantage of both a classifier and a regressor, in order to split the task-space and provide better human motion estimation accuracy with task specific models. Regarding HRI applications, we mainly focus on anthropomorphism of robot artifacts. At first we distinguish between the different notions of anthropomorphism and we introduce Functional Anthropomorphism for mapping human to anthropomorphic robot motion, respecting at the same time specific human imposed functional constraints. Then we propose a methodology for quantifying anthropomorphism of robot hands, based on set theory and computational geometry methods. This latter methodology concludes to a comprehensive score of anthropomorphism that ranges between 0 (non-humanlike) and 1 (human identical) and can be used for various robot artifacts. Subsequently, we develop a series of open-source, modular, intrinsically-compliant, low-cost, light-weight, underactuated robot hands that can be easily reproduced with off-the-self materials. The proposed hands, efficiently grasp a plethora of everyday life objects, under object pose and/or shape uncertainties and can be used for various HRI applications or even as affordable myoelectric prostheses. In order to prove the efficiency of the proposed methods, we have conducted numerous experiments involving different robot artifacts, operating in both structured and dynamic environments.Μηνάς Β. Λιαροκάπη

EthoHand: A dexterous robotic hand with ball-joint thumb enables complex in-hand object manipulation

Our dexterous hand is a fundmanetal human feature that distinguishes us from other animals by enabling us to go beyond grasping to support sophisticated in-hand object manipulation. Our aim was the design of a dexterous anthropomorphic robotic hand that matches the human hand's 24 degrees of freedom, under-actuated by seven motors. With the ability to replicate human hand movements in a naturalistic manner including in-hand object manipulation. Therefore, we focused on the development of a novel thumb and palm articulation that would facilitate in-hand object manipulation while avoiding mechanical design complexity. Our key innovation is the use of a tendon-driven ball joint as a basis for an articulated thumb. The design innovation enables our under-actuated hand to perform complex in-hand object manipulation such as passing a ball between the fingers or even writing text messages on a smartphone with the thumb's end-point while holding the phone in the palm of the same hand. We then proceed to compare the dexterity of our novel robotic hand design to other designs in prosthetics, robotics and humans using simulated and physical kinematic data to demonstrate the enhanced dexterity of our novel articulation exceeding previous designs by a factor of two. Our innovative approach achieves naturalistic movement of the human hand, without requiring translation in the hand joints, and enables teleoperation of complex tasks, such as single (robot) handed messaging on a smartphone without the need for haptic feedback. Our simple, under-actuated design outperforms current state-of-the-art prostheses or robotic and prosthetic hands regarding abilities that encompass from grasps to activities of daily living which involve complex in-hand object manipulation

Benchmarking anthropomorphic hands through grasping simulations

In recent decades, the design of anthropomorphic hands has been developed greatly improving both cosmesis and functionality. Experimentation, simulation, and combined approaches have been used in the literature to assess the effect of design alternatives (DAs) on the final performance of artificial hands. However, establishing standard benchmarks for grasping and manipulation is a need recognized among the robotics community. Experimental approaches are costly, time consuming, and inconvenient in early design stages. Alternatively, computer simulation with the adaptation of metrics based on experimental benchmarks for anthropomorphic hands could be useful to evaluate and rank DAs. The aim of this study is to compare the anthropomorphism of the grasps performed with 28 DAs of the IMMA hand, developed by the authors, using either (i) the brute-force approach and grasp quality metrics proposed in previous works or (ii) a new simulation benchmark approach. The new methodology involves the generation of efficient grasp hypotheses and the definition of a new metric to assess stability and human likeness for the most frequently used grasp types in activities of daily living, pulp pinch and cylindrical grip, adapting the experimental Anthropomorphic Hand Assessment Protocol to the simulation environment. This new simulation benchmark, in contrast to the other approach, resulted in anthropomorphic and more realistic grasps for the expected use of the objects. Despite the inherent limitations of a simulation analysis, the benchmark proposed provides interesting results for selecting optimal DAs in order to perform stable and anthropomorphic grasps

Shaping Robot Gestures to Shape Users' Perception: the Effect of Amplitude and Speed on Godspeed Ratings

This work analyses the relationship between the way robots gesture and the way those gestures are perceived by human users. In particular, this work shows how modifying the amplitude and speed of a gesture affect the Godspeed scores given to those gestures, by means of an experiment involving 45 stimuli and 30 observers. The results suggest that shaping gestures aimed at manifesting the inner state of the robot (e.g., cheering or showing disappointment) tends to change the perception of Animacy (the dimension that accounts for how driven by endogenous factors the robot is perceived to be), while shaping gestures aimed at achieving an interaction effect (e.g., engaging and disengaging) tends to change the perception of Anthropomorphism, Likeability and Perceived Safety (the dimensions that account for the social aspects of the perception)

To The Effects of Anthropomorphic Cues on Human Perception of Non-Human Robots: The Role of Gender

As non-humanoid robots increasingly permeate various sectors, understanding their design implications for human acceptance becomes paramount. Despite their ubiquity, studies on how to optimize their design for better human interaction are sparse. Our investigation, conducted through two comprehensive surveys, addresses this gap. The first survey delineated correlations between robot behavioral and physical attributes, perceived occupation suitability, and gender attributions, suggesting that both design and perceived gender significantly influence acceptance. Survey 2 delved into the effects of varying gender cues on robot designs and their consequent impacts on human-robot interactions. Our findings highlighted that distinct gender cues can bolster or impede interaction comfort

Assessment of Myoelectric Controller Performance and Kinematic Behavior of a Novel Soft Synergy-Inspired Robotic Hand for Prosthetic Applications

abstract: Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human–robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses.View the article as published at http://journal.frontiersin.org/article/10.3389/fnbot.2016.00011/ful