Human-Aware Robotics: Modeling Human Motor Skills for the Design, Planning and Control of a New Generation of Robotic Devices

Description

This book moves from a thorough investigation of human capabilities during movements and interactions with objects and environment and translates those principles into the design planning and control of innovative mechatronic systems, providing significant advancements in the fields of human–robot interaction, autonomous robots, prosthetics and assistive devices. The work presented in this monograph is characterized by a significant paradigmatic shift with respect to typical approaches, as it always place the human at the center of the technology developed, and the human represents the starting point and the actual beneficiary of the developed solutions. The content of this book is targeted to robotics and neuroscience enthusiasts, researchers and makers, students and simple lovers of the matter. Foreword Acknowledgements Contents 1 Introduction 1.1 How Do We Move? A Very Brief Historical Overview 1.2 Human Motor Control 1.2.1 Is Motion a Reflex or an Active Process? 1.3 The Problem of Dimensionality Reduction 1.4 The Implication for Robotics and the Advancement of Technologies 1.5 Open Questions and Proposed Solutions 1.6 Contents of the Monograph 1.6.1 Novelties References Part I Taming the Complexity of Human Motion Generation 2 Understanding the Principal Modes of Natural Movements in Temporal Domain 2.1 Introduction 2.2 Experimental Protocol and Setup 2.2.1 A Set of Daily Living Tasks 2.2.2 An Experimental Setup for Data Acquisition 2.3 Motion Identification 2.3.1 Modeling of Upper Limb Kinematics 2.3.2 Model Parameters 2.3.3 Markers Modeling 2.3.4 Model Calibration and Angles Estimation 2.3.5 Experimental Results 2.4 Data Analysis 2.4.1 Segmentation 2.4.2 Time Warping 2.4.3 Principal Component Analysis 2.4.4 Functional Principal Component Analysis 2.4.5 Movement Reconstruction and Performance Analysis 2.5 Conclusions and Implications for Robotics and Bioengineering References 3 Quantifying the Time-Invariance Properties of Upper Limb Synergies 3.1 Introduction 3.2 Related Work 3.3 Experimental Setup 3.3.1 Setup and Experiments 3.4 Data Analysis 3.4.1 Dynamic Time Warping 3.4.2 Repeated Principal Component Analysis 3.5 Results 3.5.1 Robustness Across Subjects and Validation 3.5.2 Principal Component Description 3.6 Discussions 3.7 Conclusions References 4 Evidences on the Hierarchical Control of Human Hands 4.1 Kinematic Domain 4.1.1 Introduction 4.1.2 Materials and Methods 4.1.3 Results 4.1.4 Discussion and Conclusions 4.2 Force Synergies in Environmental Constraint Exploitation 4.2.1 Materials and Methods 4.2.2 Results 4.2.3 Discussions and Conclusions References Part II On the Design of Nature-Inspired Prostheses and the Assessment of Motion Impairment 5 Using Nature-Inspired Principles to Design of Robotic Limbs: The Soft Wrist 5.1 Introduction 5.2 A Wrist with Synergies 5.3 Mechanical Design 5.4 Wrist Synergies in Humans 5.4.1 Experimental Procedure 5.4.2 Data Analysis 5.5 Experiments 5.5.1 System Demonstration 5.5.2 Implementation of PCs 5.6 Conclusions References 6 A Novel Approach to Quantify Motion Impairment 6.1 Introduction 6.2 Experimental Protocol and Setup 6.2.1 Set of Daily Living Tasks 6.2.2 Experimental Setup for Data Acquisition 6.2.3 Study Information 6.3 Data Analysis 6.3.1 Modeling and Pre-processing 6.3.2 Evaluation-Index of Motion Complexity 6.4 Results and Discussions 6.5 Implications and Conclusions References 7 A Novel Mechatronic System for Evaluating Elbow Muscular Spasticity Relying on Tonic Stretch Reflex Threshold Estimation 7.1 Introduction and Motivation 7.2 Spasticity and Equilibrium Point Hypothesis 7.3 Mechanical Design 7.3.1 Evaluation of Mechanical Strength 7.4 Control 7.4.1 Mode A: Position Control 7.4.2 Mode B: Torque Control 7.5 Preliminary Experiments with Healthy Subject 7.6 Conclusions References Part III Transferring Human Principles to Cobots and Autonomous Robots 8 Natural Motion: Embedding Human-Likeliness in Robot Movements 8.1 A New Method to Generate Human-Like … 8.1.1 Introduction 8.1.2 Functional Principal Components of Upper Limb Motion 8.1.3 Proposed Strategy 8.1.4 Simulations 8.1.5 Discussion and Conclusions 8.2 A Control-Based Approach to Motion Mapping 8.2.1 Introduction 8.2.2 Mapping Between Kinematics via Impedance Control 8.2.3 Experiments 8.2.4 Discussions and Conclusions References 9 A Focus on Motion Dynamics: Planning Impedance Behaviors in Physical Interaction 9.1 Introduction 9.2 Background 9.2.1 Redundant Robots 9.3 Impedance Optimization 9.3.1 Interaction Modeling 9.3.2 Robot Definition 9.3.3 Cost Function 9.3.4 Problem Complexity and Generalization to Parallel Robots 9.4 Results 9.4.1 Extension to Parallel Robots 9.5 Generalization to 3D Robots 9.6 Discussions and Conclusions References 10 Learning from Humans How to Grasp: A Reactive-Based Approach 10.1 Introduction 10.2 The Method 10.2.1 The Human-Robot Interface and Motion Capture System 10.3 Grasp Primitives 10.3.1 Data Acquisition 10.3.2 Primitive Identification 10.4 Experiments 10.4.1 Implementation 10.4.2 Experiments with Robotic Arm: Handover Task 10.4.3 Experiments with Robotic Hand: Grasping an Object from a Table 10.5 Discussions and Conclusions References 11 Learning from Humans How to Grasp: Enhancing the Reaching Strategy 11.1 Introduction 11.2 Proposed Approach 11.3 Deep Classifier 11.3.1 Object Detection 11.3.2 Primitive Classification 11.4 Robotic Grasping Primitives 11.4.1 Experimental Setup 11.4.2 Approach Phase 11.4.3 Grasp Phase 11.4.4 Control 11.5 Experimental Results 11.6 Discussion 11.7 Conclusions References 12 Learning to Prevent Grasp Failure with Soft Hands: From On-Line Prediction to Dual-Arm Grasp Recovery 12.1 Introduction 12.2 Methods 12.3 Results 12.3.1 Validation of the Neural Architecture 12.3.2 Validation of the On-Line Integrated Framework 12.4 Discussions and Conclusions References 13 Dexterity Augmentation of Robotic Hands: A Study on the Kinetic Domain 13.1 Introduction 13.2 Background 13.2.1 Modeling a Compliant Hand with Synergies 13.2.2 Optimization of Grasping Force Distribution 13.3 Materials and Methods 13.4 Results and Discussion 13.4.1 Synergy Incrementality and Optimal Hand Configuration 13.4.2 The Role of Synergy Hierarchy 13.5 Conclusions References 14 Exploiting Principal Components for Robots Walking: An Approach for Sub-Optimal Locomotion 14.1 Introduction 14.2 Problem Definition 14.3 Optimal Gait Encoding via Principal Components 14.3.1 Principal Components Analysis 14.3.2 Component Maps 14.4 Experimental Validation 14.4.1 Walking with PCs—Constant Speed 14.4.2 Walking with PCs and with PCs and CMs—Speed Variation 14.4.3 Robot Performance Evaluation 14.5 Conclusions References Appendix Conclusions and Lessons Learned