An Object-Based Approach to Map Human Hand Synergies onto Robotic Hands with Dissimilar Kinematics

Gioioso, Guido; Salvietti, Gionata; Malvezzi, Monica; Prattichizzo, Domenico

doi:10.15607/rss.2012.viii.013

Cited by 39 publications

(31 citation statements)

References 18 publications

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“…A table containing the DH parameters of each finger has to be provided to describe a hand. Four hand models are already available in the toolbox: a three finger hand, inspired by the Barrett hand, the DLR-HIT II hand [20], a three fingered modular hand [21], and a 20 DoF model of an anthropomorphic hand, referred as paradigmatic hand in [14] (Fig. 2).…”

Section: A Hand Modelingmentioning

confidence: 99%

SynGrasp: A MATLAB toolbox for grasp analysis of human and robotic hands

Malvezzi

Gioioso

Salvietti

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-SynGrasp is a MATLAB toolbox developed for the analysis of grasping, suitable both for robotic and human hands. It includes functions for the definition of hand kinematic structure and of the contact points with a grasped object. The coupling between joints induced by an underactuated control can be modeled. The hand modeling allows to define compliance at the contact, joint and actuator levels. The provided analysis functions can be used to investigate the main grasp properties: controllable forces and object displacement, manipulability analysis, grasp quality measures. Functions for the graphical representation of the hand, the object and the main analysis results are provided.

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Section: A Hand Modelingmentioning

confidence: 99%

SynGrasp: A MATLAB toolbox for grasp analysis of human and robotic hands

Malvezzi

Gioioso

Salvietti

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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“…These three approaches have been later combined [12]. The work presented in [5] shows how the pose mapping problem can be solved using a proxy object by representing the grasp by a minimal ellipsoid containing the fingertips. The inverse kinematics is then computed with respect to the shape of ellipsoid defined by the demonstrated hand pose.…”

Section: B Grasp Transfermentioning

confidence: 99%

“…This mapping is usually non-linear and it is computed either using machine learning techniques [3] or it is defined manually by exploiting the task definition using a proxy object [5] or fingertip position correspondence [18]. These ways of mapping the motion require either large amount of sample data (in the former case) or they restrict the mapping in terms of complexity of the motion or dissimilarity of the kinematics of the two systems (in the latter case).…”

Section: Introductionmentioning

confidence: 99%

Dexterous reaching, grasp transfer and planning using electrostatic representations

Sandilands

Ivan

Komura

et al. 2013

2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

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Abstract-We propose a novel approach to transfer reach and grasp movements while being agnostic and invariant to finger kinematics, hand configurations and relative changes in object dimensions. We exploit a novel representation based on electrostatics to parametrise the salient aspects of the demonstrated grasp. By working in this alternate space that focuses on the relational aspects of the grasp rather than absolute kinematics, we are able to use inference based planning techniques to couple the motion in abstract spaces with trajectories in the configuration space of the robot. We demonstrate that our method computes stable grasps that generalise over objects of different shapes and robots of dissimilar kinematics while retaining the qualitative grasp type -all without expensive collision detection or re-optimisation.

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“…In [6], [7], [8], it has been described how an object-based mapping is able to reproduce movements of a human hand model, with particular emphasis on the synergistic motion of a paradigmatic hand, in different robotic hands without considering the specific kinematics. The object-based mapping is obtained considering two virtual objects, one on the human and one on robotic hand.…”

Section: Introductionmentioning

confidence: 99%

A static intrinsically passive controller to enhance grasp stability of object-based mapping between human and robotic hands

Salvietti

Wimböck

Prattichizzo

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Replicating human hand capabilities on robotic hands is a great challenge in robotics. The high complexity of mechanical and actuation systems of available robotic device can be, however, considerably mitigated if a human inspired control is considered. In this paper the application of an objectbased mapping to the control of robot hands is presented. The basic idea is to use a virtual object, e.g. a virtual sphere, to capture human hand motion generating suitable reference signals for a low level controller of the robotic hand. The low level controller considered, which shares the idea of virtual object to reduce the complexity of the control, is the static Intrinsically Passive Controller (s-IPC). This controller is inspired by the dynamic IPC, but provides a simpler and more efficient implementation. The proposed approach allows to map motion of a human hand model, controlled on the reduced subspace of postural synergies, onto robotic hands guaranteeing the stability of the robotic grasp. This concept, which has been experimentally validated in the paper, can be exploit for complex planning methods or used in telemanipulation application.

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An Object-Based Approach to Map Human Hand Synergies onto Robotic Hands with Dissimilar Kinematics

Cited by 39 publications

References 18 publications

SynGrasp: A MATLAB toolbox for grasp analysis of human and robotic hands

SynGrasp: A MATLAB toolbox for grasp analysis of human and robotic hands

Dexterous reaching, grasp transfer and planning using electrostatic representations

A static intrinsically passive controller to enhance grasp stability of object-based mapping between human and robotic hands

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