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

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

Human-centered Electric Prosthetic (HELP) Hand

Through a partnership with Indian non-profit Bhagwan Mahaveer Viklang Sahayata Samiti, we designed a functional, robust, and and low cost electrically powered prosthetic hand that communicates with unilateral, transradial, urban Indian amputees through a biointerface. The device uses compliant tendon actuation, a small linear servo, and a wearable garment outfitted with flex sensors to produce a device that, once placed inside a prosthetic glove, is anthropomorphic in both look and feel. The prosthesis was developed such that future groups can design for manufacturing and distribution in India

Simulation-based functional evaluation of anthropomorphic artificial hands.

This thesis proposes an outline for a framework for an evaluation method that takes as an input a model of an artificial hand, which claims to be anthropomorphic, and produces as output the set of tasks that the hand can perform. The framework is based on studying the literature on the anatomy and functionalities of the human hand and methods of implementing these functionalities in artificial systems. The thesis also presents a partial implementation of the framework which focuses on tasks of gesturing and grasping using anthropomorphic postures. This thesis focuses on the evaluation of the intrinsic hardware of robot hands from technical and functional perspectives, including kinematics of the mechanical structure, geometry of the contact surface, and functional force conditions for successful grasps. This thesis does not consider topics related to control or elements of aesthetics of the design of robot hands.The thesis reviews the literature on the anatomy, motion and sensory capabilities, and functionalities of the human hand to define a reference to evaluate artificial hands. It distinguishes between the hand's construction and functionalities and presents a discussion of anthropomorphism that reflects this distinction. It reviews key theory related to artificial hands and notable solutions and existing methods of evaluating artificial hands.The thesis outlines the evaluation framework by defining the action manifold of the anthropomorphic hand, defined as the set of all tasks that a hypothetical ideal anthropomorphic hand should be able to do, and analysing the manifold tasks to determine the hand capabilities involved in the tasks and how to simulate them. A syntax is defined to describe hand tasks and anthropomorphic postures. The action manifold is defined to be used as a. functional reference to evaluate artificial hands' performance.A method to evaluate anthropomorphic postures using Fuzzy logic and a method to evaluate anthropomorphic grasping abilities are proposed and applied on models of the human hand and the InMoov robot hand. The results show the methods' ability to detect successful postures and grasps. Future work towards a full implementation of the framework is suggested

Design and development of robust hands for humanoid robots

Design and development of robust hands for humanoid robot

Advancing the Underactuated Grasping Capabilities of Single Actuator Prosthetic Hands

The last decade has seen significant advancements in upper limb prosthetics, specifically in the myoelectric control and powered prosthetic hand fields, leading to more active and social lifestyles for the upper limb amputee community. Notwithstanding the improvements in complexity and control of myoelectric prosthetic hands, grasping still remains one of the greatest challenges in robotics. Upper-limb amputees continue to prefer more antiquated body-powered or powered hook terminal devices that are favored for their control simplicity, lightweight and low cost; however, these devices are nominally unsightly and lack in grasp variety. The varying drawbacks of both complex myoelectric and simple body-powered devices have led to low adoption rates for all upper limb prostheses by amputees, which includes 35% pediatric and 23% adult rejection for complex devices and 45% pediatric and 26% adult rejection for body-powered devices [1]. My research focuses on progressing the grasping capabilities of prosthetic hands driven by simple control and a single motor, to combine the dexterous functionality of the more complex hands with the intuitive control of the more simplistic body-powered devices with the goal of helping upper limb amputees return to more active and social lifestyles. Optimization of a prosthetic hand driven by a single actuator requires the optimization of many facets of the hand. This includes optimization of the finger kinematics, underactuated mechanisms, geometry, materials and performance when completing activities of daily living. In my dissertation, I will present chapters dedicated to improving these subsystems of single actuator prosthetic hands to better replicate human hand function from simple control. First, I will present a framework created to optimize precision grasping – which is nominally unstable in underactuated configurations – from a single actuator. I will then present several novel mechanisms that allow a single actuator to map to higher degree of freedom motion and multiple commonly used grasp types. I will then discuss how fingerpad geometry and materials can better grasp acquisition and frictional properties within the hand while also providing a method of fabricating lightweight custom prostheses. Last, I will analyze the results of several human subject testing studies to evaluate the optimized hands performance on activities of daily living and compared to other commercially available prosthesis

Characterisation of Grasp Quality Metrics

Robot grasp quality metrics are used to evaluate, compare and select robotic grasp configurations. Many of them have been proposed based on a diversity of underlying principles and to assess different aspects of the grasp configurations. As a consequence, some of them provide similar information but other can provide completely different assessments. Combinations of metrics have been proposed in order to provide global indexes, but these attempts have shown the difficulties of merging metrics with different numerical ranges and even physical units. All these studies have raised the need of a deeper knowledge in order to determine independent grasp quality metrics which enable a global assessment of a grasp, and a way to combine them. This paper presents an exhaustive study in order to provide numerical evidence for these issues. Ten quality metrics are used to evaluate a set of grasps planned by a simulator for 7 different robot hands over a set of 126 object models. Three statistical analysis, namely, variability, correlation and sensitivity, are performed over this extensive database. Results and graphs presented allow to set practical thresholds for each quality metric, select independent metrics, and determine the robustness of each metric,providing a reliability indicator under pose uncertainty. The results from this paper are intended to serve as guidance for practical use of quality metrics by researchers on grasp planning algorithms

Object Handovers: a Review for Robotics

This article surveys the literature on human-robot object handovers. A handover is a collaborative joint action where an agent, the giver, gives an object to another agent, the receiver. The physical exchange starts when the receiver first contacts the object held by the giver and ends when the giver fully releases the object to the receiver. However, important cognitive and physical processes begin before the physical exchange, including initiating implicit agreement with respect to the location and timing of the exchange. From this perspective, we structure our review into the two main phases delimited by the aforementioned events: 1) a pre-handover phase, and 2) the physical exchange. We focus our analysis on the two actors (giver and receiver) and report the state of the art of robotic givers (robot-to-human handovers) and the robotic receivers (human-to-robot handovers). We report a comprehensive list of qualitative and quantitative metrics commonly used to assess the interaction. While focusing our review on the cognitive level (e.g., prediction, perception, motion planning, learning) and the physical level (e.g., motion, grasping, grip release) of the handover, we briefly discuss also the concepts of safety, social context, and ergonomics. We compare the behaviours displayed during human-to-human handovers to the state of the art of robotic assistants, and identify the major areas of improvement for robotic assistants to reach performance comparable to human interactions. Finally, we propose a minimal set of metrics that should be used in order to enable a fair comparison among the approaches.Comment: Review paper, 19 page

Human to robot hand motion mapping methods: review and classification

In this article, the variety of approaches proposed in literature to address the problem of mapping human to robot hand motions are summarized and discussed. We particularly attempt to organize under macro-categories the great quantity of presented methods, that are often difficult to be seen from a general point of view due to different fields of application, specific use of algorithms, terminology and declared goals of the mappings. Firstly, a brief historical overview is reported, in order to provide a look on the emergence of the human to robot hand mapping problem as a both conceptual and analytical challenge that is still open nowadays. Thereafter, the survey mainly focuses on a classification of modern mapping methods under six categories: direct joint, direct Cartesian, taskoriented, dimensionality reduction based, pose recognition based and hybrid mappings. For each of these categories, the general view that associates the related reported studies is provided, and representative references are highlighted. Finally, a concluding discussion along with the authors’ point of view regarding future desirable trends are reported.This work was supported in part by the European Commission’s Horizon 2020 Framework Programme with the project REMODEL under Grant 870133 and in part by the Spanish Government under Grant PID2020-114819GB-I00.Peer ReviewedPostprint (published version