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

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