Fanny Ficuciello

The DEXMART hand: Mechatronic design and experimental evaluation of synergy-based control for human-like grasping

This paper summarizes recent activities carried out for the development of an innovative anthropomorphic robotic hand called the DEXMART Hand. The main goal of this research is to face the problems that affect current robotic hands by introducing suitable design solutions aimed at achieving simplification and cost reduction while possibly enhancing robustness and performance. While certain aspects of the DEXMART Hand development have been presented in previous papers, this paper is the first to give a comprehensive description of the final hand version and its use to replicate human-like grasping. In this paper, particular emphasis is placed on the kinematics of the fingers and of the thumb, the wrist architecture, the dimensioning of the actuation system, and the final implementation of the position, force and tactile sensors. The paper focuses also on how these solutions have been integrated into the mechanical structure of this innovative robotic hand to enable precise force and displacement control of the whole system. Another important aspect is the lack of suitable control tools that severely limits the development of robotic hand applications. To address this issue, a new method for the observation of human hand behavior during interaction with common day-to-day objects by means of a 3D computer vision system is presented in this work together with a strategy for mapping human hand postures to the robotic hand. A simple control strategy based on postural synergies has been used to reduce the complexity of the grasp planning problem. As a preliminary evaluation of the DEXMART Hand’s capabilities, this approach has been adopted in this paper to simplify and speed up the transfer of human actions to the robotic hand, showing its effectiveness in reproducing human-like grasping.

Experimental evaluation of postural synergies during reach to grasp with the UB hand IV

In this paper, the postural synergies configuration subspace given by the fundamental eigengrasps of the UB Hand IV (University of Bologna Hand, version IV) is derived through experiments. This study is based on the kinematic structure of the robotic hand and on the taxonomy of the grasps of common objects. Experimental results show that it is possible to obtain grasp synthesis for a large set of objects both in the case of precision or power grasps by using only a very limited set of dominant eigengrasps. The tasks here presented are planned with an initial hold of the hand followed by reach and grasp phases, that are unique for each object/grasp combination, during which the robotic hand posture evolves continuously within a subset of the hand configuration space given by the two predominant eigenpostures. The paper reports the method adopted to define from experiments the postural synergies for the UB Hand IV and the results of the grasp tasks performed adopting the defined synergies.

Cartesian impedance control of redundant manipulators for human-robot co-manipulation

This paper addresses the problem of controlling a robot arm executing a cooperative task with a human who guides the robot through direct physical interaction. This problem is tackled by allowing the end effector to comply according to an impedance control law defined in the Cartesian space. While, in principle, the robots dynamics can be fully compensated and any impedance behaviour can be imposed by the control, the stability of the coupled human-robot system is not guaranteed for any value of the impedance parameters. Moreover, if the robot is kinematically or functionally redundant, the redundant degrees of freedom play an important role. The idea proposed here is to use redundancy to ensure a decoupled apparent inertia at the end effector. Through an extensive experimental study on a 7-DOF KUKA LWR4 arm, we show that inertial decoupling enables a more flexible choice of the impedance parameters and improves the performance during manual guidance.

A Conformable Force/Tactile Skin for Physical Human–Robot Interaction

In this letter, a new sensorized flexible skin has been used to enhance safety and intuitiveness of physical human-robot interaction (HRI) in applications where both intentional and unintentional contacts may occur. The new technological contribution with respect to other skin sensors consists of the capability of measuring both the position of the contact point and the three components of the applied force with high repeatability and accuracy. To show how this innovative technology enables the exploitation of control laws for intuitive HRI, two standard control strategies have been implemented to perform both manual guidance with multiple contact points and safe reaction in case of unintentional collision detection, at the same time. In both cases, an admittance control scheme with a second order kinematic control is adopted. A multipriority redundancy resolution strategy is implemented in the case of manual guidance. The experimental verification of the sensor capabilities is made using a patch of the skin installed on a link of a KUKA LWR4 robot.

Postural synergies of the UB Hand IV for human-like grasping

In this paper, the postural synergy configuration subspace given by the fundamental eigengrasps of the UB Hand IV is derived from experiments, and a simplified synergy-based strategy for planning grasps is proposed. The objectives of this work are, on one side, the simplification of grasp synthesis in a configuration space of reduced dimensions and, on the other side, the attainment of a human-like behavior for anthropomorphic hands. A reference set of 36 hand postures, chosen with the goal of covering the entire grasp variety referring to a recently proposed taxonomy, has been considered for the evaluation of the hand synergies. With the aim of defining general properties of the three predominant synergies, the reference set of hand postures has been applied to other two anthropomorphic robot hands, and the obtained synergies have been compared with the ones computed considering the UB Hand IV kinematics. Moreover, the synthesis of new grasps, not contained in the reference set of hand postures, has also been achieved by means of the synergy subspace. The experiments carried out demonstrate that the adopted synergy-based planning method works efficiently for all the considered grasps even if not contained in the reference set used for the evaluation of the postural synergies.

Planning and control during reach to grasp using the three predominant UB hand IV postural synergies

In this paper, a method to derive the three predominant synergies and their temporal weights for planning grasps of the UB Hand IV (University of Bologna Hand, version IV) is proposed. The method adopted to define the postural synergies from experiments is based on the kinematic structure of the robotic hand and on the taxonomy of the grasps of common objects. The control strategy, exploiting postural synergies, that drives the hand during reach to grasp is further described. During prehension the hand moves continuously in a configuration space of highly reduced dimensionality with respect to its degrees of freedom. The experiments confirm that the UB Hand IV works efficiently in a synergy based framework for grasp planning and prehension control. It is shown that the introduction of the third predominant synergy significantly improves the grasping synthesis and performance, especially for the adduction/abduction motion of the thumb.

Grasping and Control of Multi-Fingered Hands

An important issue in controlling a multi-fingered robotic hand grasping an object is the evaluation of the minimal contact forces able to guarantee the stability of the grasp and its feasibility. This problem can be solved online if suitable sensing information is available. In detail, using finger tactile information and contact force measurements, an efficient algorithm is developed to compute the optimal contact forces, assuming that, during the execution of a manipulation task, both the position of the contact points on the object and the wrench to be balanced by the contact forces may change with time. Since manipulation systems can be redundant also if the single fingers are not –due to the presence of the additional degrees of freedom (DOFs) provided by the contact variables– suitable control strategies taking advantage of such redundancy are adopted, both for single and dual-hand manipulation tasks. Another goal pursued in DEXMART is the development of a human-like grasping approach inspired to neuroscience studies. In order to simplify the synthesis of a grasp, a configuration subspace based on few predominant postural synergies of the robotic hand is computed. This approach is evaluated at kinematic level, showing that power and precise grasps can be performed using up to the third predominant synergy.

Postural Synergies and Neural Network for Autonomous Grasping: A Tool for Dextrous Prosthetic and Robotic Hands

In this paper, a neural network model has been designed for planning grasps of a cybernetic hand prototype by means of postural synergies.The synergies subspace is derived by means of a joint-to-joint mapping from a human hand set of grasps. A library of motor primitives of the hand in a synergy based rendering has been built for a number of selected objects and tasks. The requirement of the task in a simplified approach is specified by the type of grasp, such as precision or power. A feed forward neural network has been trained using the grasping data from the library and running the Levenberg-Marquadt algorithm. By combining postural synergies and neural network the nonlinear relationship between the object geometric features and the hand configuration during grasping can be easily found with a good approximation. The experiments have been performed on the DEXMART hand prototype and the results demonstrate that integration of postural synergies and neural network is a promising tool toward simplified and autonomous grasping for artificial hands, such as anthropomorphic robotic hands and prostheses.

Port-hamiltonian modeling for soft-finger manipulation

In this paper, we present a port-Hamiltonian model of a multi-fingered robotic hand, with soft-pads, while grasping and manipulating an object. The algebraic constraints of the interconnected systems are represented by a geometric object, called Dirac structure. This provides a powerful way to describe the non-contact to contact transition and contact viscoelasticity, by using the concepts of energy flows and power preserving interconnections. Using the port based model, an Intrinsically Passive Controller (IPC) is used to control the internal forces. Simulation results validate the model and demonstrate the effectiveness of the port-based approach.

A model-based strategy for mapping human grasps to robotic hands using synergies

The aim of this paper is to derive the synergies subspace of an anthropomorphic robotic hand using the human hand as a master. A set of grasping postures performed by five subjects in grasping commonly used objects has been mapped to a robotic hand assuming its own kinematics as a simplified model of the human hand. Using an RGB camera and depth sensor for 3D motion capture, the human hand palm pose and fingertip positions have been measured for the reference set of grasping. From the measured fingertip positions a closed-loop inverse kinematics algorithm has been applied to reproduce the joint space configuration of the robotic hand relying on its kinematics, scaled using the human and robotic fingers length ratio. Once the set of grasping has been mapped on the robotic hand, the synergies subspace has been computed applying principal component analysis on the joint configurations. The obtained subspace is tested with experiments on the DEXMART Hand by performing reach to grasp actions on selected objects using the first three predominant synergies. The analysis of these synergies and a comparison with the results on the human hand available in the literature are performed by means of graphical and numerical tools.