Human and robot hands : sensorimotor synergies to bridge the gap between neuroscience and robotics

This booklooks at the common problems both human and robotic hands encounter when controllingthe large number of joints, actuators and sensors required to efficientlyperform motor tasks such as object exploration, manipulation and grasping. The authorsadopt an integrated approach to explore the cont...

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Bibliographic Details
Main Authors Bianchi, Matteo, Moscatelli, Alessandro
Format eBook Book
LanguageEnglish
Published Cham Springer International Pub 2016
Springer International Publishing AG
Springer International Publishing
Edition1
SeriesSpringer Series on Touch and Haptic Systems
Subjects
Artificial Intelligence
Computer Science
Control, Robotics, Mechatronics
Haptic devices
Human-computer interaction
Human-computer interaction > Technological innovations
Neurosciences
Robot hands
Robot hands > Design and construction
User Interfaces and Human Computer Interaction
Online AccessGet full text
ISBN3319267051
9783319267050
ISSN2192-2977
2192-2985
DOI10.1007/978-3-319-26706-7

Cover

Table of Contents:
  • 12.1 Introduction -- 12.2 The SynGrasp Toolbox -- 12.2.1 How to Use SynGrasp -- 12.2.2 Hand Modelling -- 12.2.3 Grasp Definition -- 12.2.4 Grasp Analysis -- 12.3 Object-Based Mapping Using SynGrasp -- 12.4 Conclusion -- References -- 13 Quasi-Static Analysis of Synergistically Underactuated Robotic Hands in Grasping and Manipulation Tasks -- 13.1 Introduction -- 13.2 System Modeling -- 13.2.1 Object Equations -- 13.2.2 Hand Equations -- 13.2.3 Hand/Object Interaction Model -- 13.2.4 Soft Synergy Underactuation Model -- 13.2.5 The Fundamental Grasp Equation -- 13.3 Controllable System Configuration Variations -- 13.3.1 The Canonical Form of the Fundamental Grasp Equation -- 13.3.2 Relevant Properties of the Canonical Form of the Fundamental Grasp Matrix -- 13.3.3 GEROME-B: A Specialized Gauss Elimination Method for Block Partitioned Matrices -- 13.4 Solution Space Decomposition -- 13.4.1 Relevant Types of System Solutions -- 13.4.2 Discovering (Non-)Nullity Patterns in the Solution Space -- 13.5 Geometrical Interpretation of the Fundamental Grasp Equation -- 13.6 Other Types of (Under-)Actuation -- 13.7 Numerical Results -- 13.7.1 Power Grasp -- 13.8 Conclusions -- References -- 14 A Simple Model of the Hand for the Analysis of Object Exploration -- 14.1 Introduction -- 14.2 Model of the Hand -- 14.2.1 Sensors -- 14.2.2 Calibration -- 14.2.3 Calculation of a Point from a Sensor -- 14.2.4 Joint Positions -- 14.2.5 Distal (1st) phalanx -- 14.2.6 Middle (2nd) phalanx -- 14.2.7 Proximal (3rd) phalanx -- 14.2.8 Hand -- 14.3 Application Example: Contact Analysis -- 14.4 Experimental Evaluation of the Model -- 14.4.1 Participants and Apparatus -- 14.4.2 Task and Procedure -- 14.4.3 Analysis -- 14.4.4 Results -- 14.5 Discussion -- 14.5.1 Comparison with Other Models -- 14.5.2 Applications -- 14.5.3 Conclusion -- References
  • 5 Synergy Control in Subcortical Circuitry: Insights from Neurophysiology -- 5.1 Introduction -- 5.2 State-of-the-Art -- 5.3 Problem Framing -- 5.4 Synergy Control in Subcortical Circuitry -- 5.5 Conclusions -- References -- 6 Neuronal ``Op-amps'' Implement Adaptive Control in Biology and Robotics -- 6.1 Introduction -- 6.1.1 Two Central (Nervous System) Problems -- 6.1.2 Main Objective -- 6.1.3 Scope and Assumptions -- 6.1.4 Outline -- 6.2 The Neuronal Op-amp -- 6.2.1 Plasticity of Neuronal Op-amps -- 6.2.2 Internal Model Control Using Neuronal Op-amps -- 6.3 Experiment: Neuronal Op-amps in Biology -- 6.3.1 Setup -- 6.3.2 Execution -- 6.3.3 Results -- 6.4 Experiment: Neuronal Op-amps in Engineering -- 6.4.1 Setup -- 6.4.2 Execution -- 6.4.3 Results -- 6.5 Discussion and Conclusions -- References -- 7 Sensorymotor Synergies: Fusion of Cutaneous Touch and Proprioception in the Perceived Hand Kinematics -- 7.1 Introduction -- 7.2 Contact Area -- 7.2.1 Methods -- 7.2.2 Results -- 7.3 Slip Motion -- 7.3.1 Methods -- 7.3.2 Results -- 7.4 Discussion -- References -- Part II Robotics, Models and Sensing Tools -- 8 From Soft to Adaptive Synergies: The Pisa/IIT SoftHand -- 8.1 Introduction -- 8.2 Hand Actuation, Synergies and Adaptation -- 8.2.1 Fully Actuated Hands -- 8.2.2 Approaches to Simplification -- 8.2.3 Soft Synergies -- 8.2.4 Adaptive Synergies -- 8.2.5 From Soft to Adaptive Synergies -- 8.3 The Pisa/IIT SoftHand -- 8.4 Experimental Results -- 8.4.1 Force and Torque Measurements -- 8.4.2 Grasp Experiments -- 8.5 A New Set of Possibilities -- 8.6 Conclusion -- References -- 9 A Learn by Demonstration Approach for Closed-Loop, Robust, Anthropomorphic Grasp Planning -- 9.1 Introduction -- 9.2 Apparatus and Kinematic Models -- 9.2.1 Mitsubishi PA 10 DLR/HIT II Robot Arm Hand System -- 9.2.2 Tactile Sensors -- 9.2.3 Motion Capture Systems
  • Intro -- Series Editors' Foreword -- Contents -- Contributors -- 1 Introduction -- References -- Part I Neuroscience -- 2 Dexterous Manipulation: From High-Level Representation to Low-Level Coordination of Digit Forces and Positions -- 2.1 Introduction -- 2.2 Materials and Methods -- 2.3 Experiment 1: Digit Force and Position Coordination in Unconstrained Grasping -- 2.4 Experiment 2: Transfer of Learned Manipulation Between Different Grip Types -- 2.5 Discussion -- 2.5.1 Redundancy of Kinematic and Kinetic Solutions Through Digit Force-to-Position Modulation -- 2.5.2 High-Level Representation of Learned Manipulation -- 2.5.3 Open Questions and Future Research -- References -- 3 Digit Position and Force Synergies During Unconstrained Grasping -- 3.1 Introduction -- 3.2 Methods -- 3.2.1 Participants -- 3.2.2 Hardware -- 3.2.3 Procedure -- 3.3 Data Processing and Analysis -- 3.4 Results -- 3.4.1 Center of Pressure for Individual Participants -- 3.4.2 Digit Normal Forces Versus CoPs -- 3.4.3 Digit Forces Synergies -- 3.5 Discussion -- References -- 4 The Motor Control of Hand Movements in the Human Brain: Toward the Definition of a Cortical Representation of Postural Synergies -- 4.1 Introduction -- 4.2 Action Processing in the Brain -- 4.3 A Cortical Network for Hand Posture Control -- 4.4 The Network for Hand Control in Humans: fMRI Evidences -- 4.5 Somatotopic Control of Hand Muscles -- 4.6 ``Languages'' of Hand Control in Primary Motor Cortex -- 4.7 Synergies and Their Brain Correlates -- 4.8 Alternative Hypotheses: A Revised Somatotopy? -- 4.9 Techniques for Hand Movement Recordings: Motion Capture and EMG -- 4.10 Encoding Techniques: Integrating Behavioral and fMRI Data -- 4.11 Combining Techniques to ``Decode'' Hand Posture -- 4.12 Description and Preliminary Results -- 4.13 Conclusions and Future Directions -- References
  • 15 Synergy-Based Optimal Sensing Techniques for Hand Pose Reconstruction -- 15.1 Introduction -- 15.2 Biology and Artificial Systems: A Mutual Inspiration -- 15.3 Performance Enhancement -- 15.3.1 The Hand Posture Estimation Algorithm -- 15.3.2 Data Acquisition -- 15.3.3 Experimental Results -- 15.4 Optimal Design -- 15.4.1 Problem Definition -- 15.4.2 Continuous Sensing Design -- 15.4.3 Discrete Sensing Design -- 15.4.4 Hybrid Sensing Design -- 15.4.5 Continuous and Discrete Sensing Optimal Distribution -- 15.4.6 Estimation Results with Optimal Discrete Sensing Devices -- 15.5 Conclusions and Future Works -- References
  • 9.2.4 Kinematic Model of the Human Arm Hand System -- 9.3 Learn by Demonstration for Closed Loop, Anthropomorphic Grasp Planning -- 9.3.1 Learn by Demonstration Experiments -- 9.3.2 Mapping Human to Robot Motion with Functional Anthropomorphism -- 9.3.3 Learning Navigation Function Models in the Anthropomorphic Robot Low-D Space -- 9.3.4 A Vision System Based on RGB-D Cameras -- 9.4 Task Specific, Robust Grasping with Tactile Sensing -- 9.4.1 A Scheme for Deriving Task Specific Grasping Postures -- 9.4.2 A Scheme that Provides Optimal Force Transmission and Robustness Against Positioning Inaccuracies -- 9.4.3 A Grasping Force Optimization Scheme Utilizing Tactile Sensing -- 9.5 Results and Experimental Validation -- 9.5.1 Closed-Loop, Anthropomorphic Grasp Planning Scenario -- 9.5.2 Task-Specific, Robust Grasping Scenario -- 9.6 Conclusions and Discussion -- References -- 10 Teleimpedance Control: Overview and Application -- 10.1 Teleimpedance Control -- 10.2 Application -- 10.2.1 Teleimpedance Control of a Robotic Arm -- 10.2.2 Teleimpedance Control of a Robotic Hand -- References -- 11 Incremental Learning of Muscle Synergies: From Calibration to Interaction -- 11.1 Introduction -- 11.2 Background -- 11.2.1 Muscle Activations in Prosthetic Control -- 11.2.2 Unreliability -- 11.2.3 Building a More Detailed Model or Learning More? -- 11.2.4 Incremental/Interactive Learning -- 11.3 A Practical Method of Incremental Learning -- 11.3.1 Monolithic Learning in the Linear Case -- 11.3.2 Extension to the Non-linear Case -- 11.3.3 Incrementality -- 11.3.4 Obtaining Ground Truth -- 11.3.5 Applications -- 11.4 Discussion -- 11.4.1 On the Capacity of Incremental Learning -- 11.4.2 Relation to Muscle Synergies as Traditionally Defined -- 11.5 Conclusions -- References -- 12 How to Map Human Hand Synergies onto Robotic Hands Using the SynGrasp Matlab Toolbox