Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation

✍ Scribed by Qiang Li (editor), Shan Luo (editor), Zhaopeng Chen (editor), Chenguang Yang (editor), Jianwei Zhang (editor)

• Publisher: Academic Press
• Year: 2022
• Tongue: English
• Leaves: 374
• Edition: 1
• Category: Library

✦ Synopsis

Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation focuses on cross-disciplinary lines of research and groundbreaking research ideas in three research lines: tactile sensing, skill learning and dexterous control. The book introduces recent work about human dexterous skill representation and learning, along with discussions of tactile sensing and its applications on unknown objects’ property recognition and reconstruction. Sections also introduce the adaptive control schema and its learning by imitation and exploration. Other chapters describe the fundamental part of relevant research, paying attention to the connection among different fields and showing the state-of-the-art in related branches.

The book summarizes the different approaches and discusses the pros and cons of each. Chapters not only describe the research but also include basic knowledge that can help readers understand the proposed work, making it an excellent resource for researchers and professionals who work in the robotics industry, haptics and in machine learning.

✦ Table of Contents

Front Cover
Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation
Copyright
Contents
Contributors
Preface
Part I Tactile sensing and perception
1 GelTip tactile sensor for dexterous manipulation in clutter
1.1 Introduction
1.2 An overview of the tactile sensors
1.2.1 Marker-based optical tactile sensors
1.2.2 Image-based optical tactile sensors
1.3 The GelTip sensor
1.3.1 Overview
1.3.2 The sensor projective model
1.3.3 Fabrication process
1.4 Evaluation
1.4.1 Contact localization
1.4.2 Touch-guided grasping in a Blocks World environment
1.5 Conclusions and discussion
Acknowledgment
References
2 Robotic perception of object properties using tactile sensing
2.1 Introduction
2.2 Material properties recognition using tactile sensing
2.3 Object shape estimation using tactile sensing
2.4 Object pose estimation using tactile sensing
2.5 Grasping stability prediction using tactile sensing
2.6 Vision-guided tactile perception for crack reconstruction
2.6.1 Visual guidance for touch sensing
2.6.2 Guided tactile crack perception
2.6.3 Experimental setup
2.6.4 Experimental results
2.7 Conclusion and discussion
References
3 Multimodal perception for dexterous manipulation
3.1 Introduction
3.2 Visual-tactile cross-modal generation
3.2.1 Touching to see'' andseeing to feel''
3.2.2 Experimental results
3.3 Spatiotemporal attention model for tactile texture perception
3.3.1 Spatiotemporal attention model
3.3.2 Spatial attention
3.3.3 Temporal attention
3.3.4 Experimental results
3.3.5 Attention distribution visualization
3.4 Conclusion and discussion
Acknowledgment
References
4 Capacitive material detection with machine learning for robotic grasping applications
4.1 Introduction
4.1.1 Motivation
4.1.2 Concept
4.1.3 Related work
4.2 Basic knowledge
4.2.1 Capacitance perception
4.2.1.1 Sensing hardware
4.2.1.2 Signal processing
4.2.1.3 Capacitance spectroscopy
4.2.2 Classification for material detection
4.2.2.1 k-Nearest neighbors
4.2.2.2 Support vector machines
4.2.2.3 Random forest classifier
4.2.2.4 Feedforward neural networks
4.2.2.5 Convolutional neural networks
4.3 Methods
4.3.1 Data preparation
4.3.1.1 Raw data
4.3.1.2 Image generation
4.3.2 Classifier configurations
4.4 Experiments
4.5 Conclusion
References
Part II Skill representation and learning
5 Admittance control: learning from humans through collaborating with humans
5.1 Introduction
5.2 Learning from human based on admittance control
5.2.1 Learning a task using dynamic movement primitives
5.2.1.1 Constructing a second-order nonlinear system
5.2.1.2 Learning the DMPs model
5.2.2 Admittance control model
5.2.3 Learning of compliant movement profiles based on biomimetic control
5.2.3.1 Robotic compliant movement representation
5.2.3.2 Adaptation law
5.3 Experimental validation
5.3.1 Simulation task
5.3.2 Handover task
5.3.3 Sawing task
5.4 Human robot collaboration based on admittance control
5.4.1 Principle of human arm impedance model
5.4.2 Estimation of stiffness matrix
5.4.3 Stiffness mapping between human and robot arm
5.5 Variable admittance control model
5.6 Experiments
5.6.1 Test of variable admittance control
5.6.2 Human–robot collaborative sawing task
5.7 Conclusion
References
6 Sensorimotor control for dexterous grasping – inspiration from human hand
6.1 Introduction of sensorimotor control for dexterous grasping
6.2 Sensorimotor control for grasping kinematics
6.3 Sensorimotor control for grasping kinetics
6.4 Conclusions
Acknowledgments
References
7 From human to robot grasping: force and kinematic synergies
7.1 Introduction
7.1.1 Human hand synergies
7.1.2 The impact of the synergies approach on robotic hands
7.2 Experimental studies
7.2.1 Study 1: force synergies comparison between human and robot hands
7.2.2 Results of force synergies study
7.2.3 Study 2: kinematic synergies in both human and robot hands
7.2.4 Results of kinematic synergies study
7.3 Discussion
7.3.1 Force synergies: human vs. robot
7.3.2 Kinematic synergies: human vs. robot
7.4 Conclusions
Acknowledgments
References
8 Learning form-closure grasping with attractive region in environment
8.1 Background
8.2 Related work
8.2.1 Closure properties
8.2.2 Environmental constraints
8.2.3 Learning to grasp
8.3 Learning a form-closure grasp with attractive region in environment
8.3.1 Attractive region in environment for four-pin grasping
8.3.2 Learning to evaluate grasp quality with ARIE
8.3.2.1 Formation of the grasp quality measurement function
8.3.2.2 Uncertainties during grasping process
8.3.2.3 Calculation of the grasp quality score
8.3.2.4 Training of the network for grasp quality measurement
8.3.3 Learning to grasp with ARIE
8.3.3.1 Formation of the learning pipeline
8.3.3.2 Learning policy
8.3.3.3 Reward
8.4 Conclusion
References
9 Learning hierarchical control for robust in-hand manipulation
9.1 Introduction
9.2 Related work
9.3 Methodology
9.3.1 Hierarchical structure for in-hand manipulation
9.3.2 Low-level controller
9.3.3 Mid-level controller
9.4 Experiments
9.4.1 Training mid-level policies and baseline
9.4.2 Dataset
9.4.3 Reaching desired object poses
9.4.4 Robustness analysis
9.4.5 Manipulating a cube
9.5 Conclusion
References
10 Learning industrial assembly by guided-DDPG
10.1 Introduction
10.2 From model-free RL to model-based RL
10.2.1 Guided policy search
10.2.2 Deep deterministic policy gradient
10.2.3 Comparison of DDPG and GPS
10.3 Guided deep deterministic policy gradient
10.4 Simulations and experiments
10.4.1 Parameter lists
10.4.2 Simulation results
10.4.2.1 Comparison of different supervision methods
10.4.2.2 Effects of the supervision weight wto
10.4.2.3 Comparison of different algorithms
10.4.2.4 Adaptability of the learned policy
10.4.3 Experimental results
10.5 Chapter summary
References
Part III Robotic hand adaptive control
11 Clinical evaluation of Hannes: measuring the usability of a novel polyarticulated prosthetic hand
11.1 Introduction
11.2 Preliminary study
11.2.1 Data collection
11.2.1.1 Questionnaire
11.2.1.2 Focus group
11.2.2 Outcomes
11.3 The Hannes system
11.3.1 Analysis of survey study and definition of requirements
11.3.2 System architecture
11.3.2.1 The Hannes hand
11.3.2.2 Custom EMG sensors
11.4 Pilot study for evaluating the Hannes hand
11.4.1 Materials and methods
11.4.1.1 Subjects
11.4.1.2 Study protocol
11.4.1.3 Clinical evaluation measures
11.4.2 Results
11.5 Validation of custom EMG sensors
11.5.1 Materials and methods
11.5.1.1 Subjects
11.5.1.2 Study protocol
11.5.1.3 Analysis
11.5.2 Results
11.6 Discussion and conclusions
References
12 A hand-arm teleoperation system for robotic dexterous manipulation
12.1 Introduction
12.2 Problem formulation
12.3 Vision-based teleoperation for dexterous hand
12.3.1 Transteleop
12.3.2 Pair-wise robot–human hand dataset generation
12.4 Hand-arm teleoperation system
12.5 Transteleop evaluation
12.5.1 Network implementation details
12.5.2 Transteleop evaluation
12.5.3 Hand pose analysis
12.6 Manipulation experiments
12.7 Conclusion and discussion
References
13 Neural network-enhanced optimal motion planning for robot manipulation under remote center of motion
13.1 Introduction
13.2 Problem statement
13.2.1 Kinematics modeling
13.2.2 RCM constraint
13.2.2.1 2D RCM constraint
13.2.2.2 3D RCM constraint
13.3 Control system design
13.3.1 Controller design method
13.3.2 RBFNN-based approximation
13.3.3 Control framework
13.4 Simulation results
13.5 Conclusion
References
14 Towards dexterous in-hand manipulation of unknown objects
14.1 Introduction
14.2 State of the art
14.3 Reactive object manipulation framework
14.3.1 Local manipulation controller – position part
14.3.2 Local manipulation controller – force part
14.3.3 Local manipulation controller – composite part
14.3.4 Regrasp planner
14.4 Finding optimal regrasp points
14.4.1 Grasp stability and manipulability
14.4.2 Object surface exploration controller
14.5 Evaluation in physics-based simulation
14.5.1 Local object manipulation
14.5.2 Large-scale object manipulation
14.6 Evaluation in a real robot experiment
14.6.1 Unknown object surface exploration by one finger
14.6.2 Unknown object local manipulation by two fingers
14.7 Summary and outlook
Acknowledgment
References
15 Robust dexterous manipulation and finger gaiting under various uncertainties
15.1 Introduction
15.2 Dual-stage manipulation and gaiting framework
15.3 Modeling of uncertain manipulation dynamics
15.3.1 State-space dynamics
15.3.2 Combining feedback linearization with modeling
15.4 Robust manipulation controller design
15.4.1 Design scheme
15.4.2 Design of weighting functions
15.4.2.1 Design of performance weighting function Wperf
15.4.2.2 Design of action weighting function Wu
15.4.2.3 Design of disturbance weighting function Wdis
15.4.2.4 Design of noise weighting function Wn
15.4.3 Manipulation controller design
15.5 Real-time finger gaits planning
15.5.1 Grasp quality analysis
15.5.2 Position-level finger gaits planning
15.5.3 Velocity-level finger gaits planning
15.5.4 Similarities between position-level and velocity-level planners
15.5.5 Finger gaiting with jump control
15.6 Simulation and experiment studies
15.6.1 Simulation setup
15.6.2 Experimental setup
15.6.3 Parameter lists
15.6.3.1 RMC parameters for simulation test
15.6.3.2 RMC parameters for BarrettHand experiment
15.6.3.3 Parameters for finger gaits planner simulation
15.6.4 RMC simulation results
15.6.4.1 Comparison with different methods
15.6.4.2 Robustness to uncertainties
15.6.5 RMC experiment results
15.6.6 Finger gaiting simulation results
15.6.6.1 Finger gaiting on smooth surfaces under uncertainties
15.6.6.2 Finger gaiting of a three-fingered hand
15.7 Chapter summary
References
A Key components of dexterous manipulation: tactile sensing, skill learning, and adaptive control
A.1 Introduction
A.2 Why sensing, why tactile sensing
A.3 Why skill learning
A.4 Why adaptive control
A.5 Conclusion
Index
Back Cover