Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives

In this paper we review the works related to muscle synergies that have been carried-out in neuroscience and control engineering. In particular, we refer to the hypothesis that the central nervous system (CNS) generates desired muscle contractions by combining a small number of predefined modules, called muscle synergies. We provide an overview of the methods that have been employed to test the validity of this scheme, and we show how the concept of muscle synergy has been generalized for the control of artificial agents. The comparison between these two lines of research, in particular their different goals and approaches, is instrumental to explain the computational implications of the hypothesized modular organization. Moreover, it clarifies the importance of assessing the functional role of muscle synergies: although these basic modules are defined at the level of muscle activations (input-space), they should result in the effective accomplishment of the desired task. This requirement is not always explicitly considered in experimental neuroscience, as muscle synergies are often estimated solely by analyzing recorded muscle activities. We suggest that synergy extraction methods should explicitly take into account task execution variables, thus moving from a perspective purely based on input-space to one grounded on task-space as well

Planning hand-arm grasping motions with human-like appearance

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksFinalista de l’IROS Best Application Paper Award a la 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, ICROS.This paper addresses the problem of obtaining human-like motions on hand-arm robotic systems performing pick-and-place actions. The focus is set on the coordinated movements of the robotic arm and the anthropomorphic mechanical hand, with which the arm is equipped. For this, human movements performing different grasps are captured and mapped to the robot in order to compute the human hand synergies. These synergies are used to reduce the complexity of the planning phase by reducing the dimension of the search space. In addition, the paper proposes a sampling-based planner, which guides the motion planning ollowing the synergies. The introduced approach is tested in an application example and thoroughly compared with other state-of-the-art planning algorithms, obtaining better results.Peer ReviewedAward-winningPostprint (author's final draft

Adaptive Synergies for the Design and Control of the Pisa/IIT SoftHand

In this paper we introduce the Pisa/IIT SoftHand, a novel robot hand prototype designed with the purpose of being robust and easy to control as an industrial gripper, while exhibiting high grasping versatility and an aspect similar to that of the human hand. In the paper we briefly review the main theoretical tools used to enable such simplification, i.e. the neuroscience-based notion of soft synergies. A discussion of several possible actuation schemes shows that a straightforward implementation of the soft synergy idea in an effective design is not trivial. The approach proposed in this paper, called adaptive synergy, rests on ideas coming from underactuated hand design. A synthesis method to realize a desired set of soft synergies through the principled design of adaptive synergy is discussed. This approach leads to the design of hands accommodating in principle an arbitrary number of soft synergies, as demonstrated in grasping and manipulation simulations and experiments with a prototype. As a particular instance of application of the synthesis method of adaptive synergies, the Pisa/IIT SoftHand is described in detail. The hand has 19 joints, but only uses 1 actuator to activate its adaptive synergy. Of particular relevance in its design is the very soft and safe, yet powerful and extremely robust structure, obtained through the use of innovative articulations and ligaments replacing conventional joint design. The design and implementation of the prototype hand are shown and its effectiveness demonstrated through grasping experiments, reported also in multimedia extensio

Bio-Artificial Synergies for Grasp Posture Control of Supernumerary Robotic Fingers

A new type of wrist-mounted robot, the Supernumerary Robotic (SR) Fingers, is proposed to work closely with the human hand and aid the human in performing a variety of prehensile tasks. For people with diminished functionality of their hands, these robotic fingers could provide the opportunity to live with more independence and work more productively. A natural and implicit coordination between the SR Fingers and the human fingers is required so the robot can be transformed to act as part of the human body. This paper presents a novel control algorithm, termed “Bio-Artificial Synergies”, which enables the SR and human fingers to share the task load together and adapt to diverse task conditions. Through grasp experiments and data analysis, postural synergies were found for a seven-fingered hand comprised of two SR Fingers and five human fingers. The synergy-based control law was then extracted from the experimental data using Partial Least Squares (PLS) regression and tested on the SR Finger prototype as a proof of concept

On Grasp Quality Measures: Grasp Robustness and Contact Force Distribution in Underactuated and Compliant Robotic Hands

The availability of grasp quality measures is fundamental for grasp planning and control, and also to drive designers in the definition and optimization of robotic hands. This work investigates on grasp robustness and quality indexes that can be applied to power grasps with underactuated and compliant hands. When dealing with such types of hands, there is the need of an evaluation method that takes into account the forces that can be actually controlled by the hand, depending on its actuation system. In this paper, we study the potential contact robustness and the potential grasp robustness (PCR, PGR) indexes. They both consider main grasp properties: contact points, friction coefficient, etc., but also hand degrees of freedom and consequently, the directions of controllable contact forces. The PCR comes directly from the classical grasp theory and can be easily evaluated, but often leads to too conservative solutions, particularly when the grasp has many contacts. The PGR is more complex and computationally heavier, but gives a more realistic, even if still conservative, estimation of the overall grasp robustness, also in power grasps. We evaluated the indexes for various simulated grasps, performed with underactuated and compliant hands, and we analyzed their variations with respect to the main grasp parameters

On the synthesis of feasible and prehensile robotic grasps

Trabajo presentado al ICRA celebrado en Minnesota del 14 al 18 de mayo de 2012.This work proposes a solution to the grasp synthesis problem, which consist of finding the best hand configuration to grasp a given object for a specific manipulation task while satisfying all the necessary constraints. This problem had been divided into sequential sub-problems, including contact region determination, hand inverse kinematics and force distribution, with the particular constraints of each step tackled independently. This may lead to unnecessary effort, such as when one of the problems has no solution given the output of the previous step as input. To overcome this issue, we present a kinestatic formulation of the grasp synthesis problem that introduces compliance both at the joints and the contacts. This provides a proper framework to synthesize a feasible and prehensile grasp by considering simultaneously the necessary grasping constraints, including contact reachability, object restraint, and force controllability. As a consequence, a solution of the proposed model results in a set of hand configurations that allows to execute the grasp using only a position controller. The approach is illustrated with experiments on a simple planar hand using two fingers and an anthropomorphic robotic hand using three fingers.This work was partially supported by the CICYT projects DPI2010-18449, DPI2010-15446 and DPI2011-22471, and by the European Commission under CP grants no. 248587, “THE Hand Embodied”, and no. 270350, “ROBLOG”, within the FP7-ICT-2009-4-2-1 program “Cognitive Systems and Robotics”.Peer Reviewe

Synergy-based Hand Pose Sensing: Reconstruction Enhancement

Low-cost sensing gloves for reconstruction posture provide measurements which are limited under several regards. They are generated through an imperfectly known model, are subject to noise, and may be less than the number of Degrees of Freedom (DoFs) of the hand. Under these conditions, direct reconstruction of the hand posture is an ill-posed problem, and performance can be very poor. This paper examines the problem of estimating the posture of a human hand using(low-cost) sensing gloves, and how to improve their performance by exploiting the knowledge on how humans most frequently use their hands. To increase the accuracy of pose reconstruction without modifying the glove hardware - hence basically at no extra cost - we propose to collect, organize, and exploit information on the probabilistic distribution of human hand poses in common tasks. We discuss how a database of such an a priori information can be built, represented in a hierarchy of correlation patterns or postural synergies, and fused with glove data in a consistent way, so as to provide a good hand pose reconstruction in spite of insufficient and inaccurate sensing data. Simulations and experiments on a low-cost glove are reported which demonstrate the effectiveness of the proposed techniques.Comment: Submitted to International Journal of Robotics Research (2012

Replicating human hand synergies onto robotic hands: a review on software and hardware strategies

This review reports the principal solutions proposed in the literature to reduce the complexity of the control and of the design of robotic hands taking inspiration from the organization of the human brain. Several studies in neuroscience concerning the sensorimotor organization of the human hand proved that, despite the complexity of the hand, a few parameters can describe most of the variance in the patterns of configurations and movements. In other words, humans exploit a reduced set of parameters, known in the literature as synergies, to control their hands. In robotics, this dimensionality reduction can be achieved by coupling some of the degrees of freedom (DoFs) of the robotic hand, that results in a reduction of the needed inputs. Such coupling can be obtained at the software level, exploiting mapping algorithm to reproduce human hand organization, and at the hardware level, through either rigid or compliant physical couplings between the joints of the robotic hand. This paper reviews the main solutions proposed for both the approaches