by Lo, Ka-wah. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 76-81 (2nd gp.)). / Acknowledgments --- p.i / Abstract --- p.ii / Notations --- p.iii / List of Figures --- p.v / List of Tables --- p.vii / Chapter 1. --- Introduction / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Literature Review --- p.4 / Chapter 1.3 --- Objective --- p.10 / Chapter 2. --- Comparison of Different Approaches / Chapter 2.1 --- Sample Manipulator --- p.11 / Chapter 2.1.1 --- Force Decomposition Method --- p.12 / Chapter 2.1.2 --- Forward Rate Kinematics Base Method --- p.15 / Chapter 2.1.3 --- Grassmann Geometry Method --- p.18 / Chapter 2.2 --- Comparison Criteria --- p.20 / Chapter 2.2.1 --- Computational Complexity --- p.20 / Chapter 2.2.2 --- Scope of Application --- p.22 / Chapter 2.3 --- Summary --- p.23 / Chapter 3. --- Enumeration of Configuration Singularity / Chapter 3.1 --- Novel 6 DOF --- p.25 / Chapter 3.1.1 --- Result Analysis --- p.31 / Chapter 3.2 --- A 3 DOF with Symmetric Base --- p.33 / Chapter 3.2.1 --- Result Analysis --- p.35 / Chapter 3.3 --- A 3 DOF with Non-Symmetric Base --- p.36 / Chapter 3.3.1 --- Result Analysis --- p.37 / Chapter 3.4 --- A New Model of 6-SPS Defined by Kong et al --- p.40 / Chapter 3.5 --- A New Class of 6-SPS Platform-Type Parallel Manipulator --- p.45 / Chapter 3.5.1 --- The Hexagonal Base --- p.46 / Chapter 3.5.2 --- The Pentagonal Base --- p.50 / Chapter 3.5.3 --- The Tetragonal Base --- p.52 / Chapter 3.5.4 --- The Triangular Base --- p.55 / Chapter 3.6 --- Summary --- p.59 / Chapter 4. --- Numerical Analysis / Chapter 4.1 --- Parameter Analysis --- p.60 / Chapter 4.1.1 --- One Unknown Variable --- p.61 / Chapter 4.1.2 --- Two Unknown Variables --- p.63 / Chapter 4.2 --- Critical Value of Ratio R/q --- p.69 / Chapter 4.3 --- Summary --- p.72 / Chapter 5. --- Conclusions and Future Work / Chapter 5.1 --- Conclusions --- p.73 / Chapter 5.2 --- Future Work --- p.75 / References --- p.76 / Appendix --- p.82

Fok Lo Ming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 133-135). / Abstracts in English and Chinese. / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Multi-fingered Robotic Hands --- p.1 / Chapter 1.2 --- Grasping Force Optimization --- p.2 / Chapter 1.3 --- Neural Networks --- p.6 / Chapter 1.4 --- Previous Work for Grasping Force Optimization --- p.9 / Chapter 1.5 --- Contributions of this work --- p.10 / Chapter 1.6 --- Organization of this thesis --- p.12 / Chapter 2. --- Problem Formulations --- p.13 / Chapter 2.1 --- Grasping Force Optimization without Joint Torque Limits --- p.14 / Chapter 2.1.1 --- Linearized Friction Cone Approach --- p.15 / Chapter i. --- Linear Formulation --- p.17 / Chapter ii. --- Quadratic Formulation --- p.18 / Chapter 2.1.2 --- Nonlinear Friction Cone as Positive Semidefinite Matrix --- p.19 / Chapter 2.1.3 --- Constrained Optimization with Nonlinear Inequality Constraint --- p.20 / Chapter 2.2 --- Grasping Force Optimization with Joint Torque Limits --- p.21 / Chapter 2.2.1 --- Linearized Friction Cone Approach --- p.23 / Chapter 2.2.2 --- Constrained Optimization with Nonlinear Inequality Constraint --- p.23 / Chapter 2.3 --- Grasping Force Optimization with Time-varying External Wrench --- p.24 / Chapter 2.3.1 --- Linearized Friction Cone Approach --- p.25 / Chapter 2.3.2 --- Nonlinear Friction Cone as Positive Semidefinite Matrix --- p.25 / Chapter 2.3.3 --- Constrained Optimization with Nonlinear Inequality Constraint --- p.26 / Chapter 3. --- Recurrent Neural Network Models --- p.27 / Chapter 3.1 --- Networks for Grasping Force Optimization without Joint Torque Limits / Chapter 3.1.1 --- The Primal-dual Network for Linear Programming --- p.29 / Chapter 3.1.2 --- The Deterministic Annealing Network for Linear Programming --- p.32 / Chapter 3.1.3 --- The Primal-dual Network for Quadratic Programming --- p.34 / Chapter 3.1.4 --- The Dual Network --- p.35 / Chapter 3.1.5 --- The Deterministic Annealing Network --- p.39 / Chapter 3.1.6 --- The Novel Network --- p.41 / Chapter 3.2 --- Networks for Grasping Force Optimization with Joint Torque Limits / Chapter 3.2.1 --- The Dual Network --- p.43 / Chapter 3.2.2 --- The Novel Network --- p.45 / Chapter 3.3 --- Networks for Grasping Force Optimization with Time-varying External Wrench / Chapter 3.3.1 --- The Primal-dual Network for Quadratic Programming --- p.48 / Chapter 3.3.2 --- The Deterministic Annealing Network --- p.50 / Chapter 3.3.3 --- The Novel Network --- p.52 / Chapter 4. --- Simulation Results --- p.54 / Chapter 4.1 --- Three-finger Grasping Example of Grasping Force Optimization without Joint Torque Limits --- p.54 / Chapter 4.1.1 --- The Primal-dual Network for Linear Programming --- p.57 / Chapter 4.1.2 --- The Deterministic Annealing Network for Linear Programming --- p.59 / Chapter 4.1.3 --- The Primal-dual Network for Quadratic Programming --- p.61 / Chapter 4.1.4 --- The Dual Network --- p.63 / Chapter 4.1.5 --- The Deterministic Annealing Network --- p.65 / Chapter 4.1.6 --- The Novel Network --- p.57 / Chapter 4.1.7 --- Network Complexity Analysis --- p.59 / Chapter 4.2 --- Four-finger Grasping Example of Grasping Force Optimization without Joint Torque Limits --- p.73 / Chapter 4.2.1 --- The Primal-dual Network for Linear Programming --- p.75 / Chapter 4.2.2 --- The Deterministic Annealing Network for Linear Programming --- p.77 / Chapter 4.2.3 --- The Primal-dual Network for Quadratic Programming --- p.79 / Chapter 4.2.4 --- The Dual Network --- p.81 / Chapter 4.2.5 --- The Deterministic Annealing Network --- p.83 / Chapter 4.2.6 --- The Novel Network --- p.85 / Chapter 4.2.7 --- Network Complexity Analysis --- p.87 / Chapter 4.3 --- Three-finger Grasping Example of Grasping Force Optimization with Joint Torque Limits --- p.90 / Chapter 4.3.1 --- The Dual Network --- p.93 / Chapter 4.3.2 --- The Novel Network --- p.95 / Chapter 4.3.3 --- Network Complexity Analysis --- p.97 / Chapter 4.4 --- Three-finger Grasping Example of Grasping Force Optimization with Time-varying External Wrench --- p.99 / Chapter 4.4.1 --- The Primal-dual Network for Quadratic Programming --- p.101 / Chapter 4.4.2 --- The Deterministic Annealing Network --- p.103 / Chapter 4.4.3 --- The Novel Network --- p.105 / Chapter 4.4.4 --- Network Complexity Analysis --- p.107 / Chapter 4.5 --- Four-finger Grasping Example of Grasping Force Optimization with Time-varying External Wrench --- p.109 / Chapter 4.5.1 --- The Primal-dual Network for Quadratic Programming --- p.111 / Chapter 4.5.2 --- The Deterministic Annealing Network --- p.113 / Chapter 4.5.3 --- The Novel Network --- p.115 / Chapter 5.5.4 --- Network Complexity Analysis --- p.117 / Chapter 4.6 --- Four-finger Grasping Example of Grasping Force Optimization with Nonlinear Velocity Variation --- p.119 / Chapter 4.5.1 --- The Primal-dual Network for Quadratic Programming --- p.121 / Chapter 4.5.2 --- The Deterministic Annealing Network --- p.123 / Chapter 4.5.3 --- The Novel Network --- p.125 / Chapter 5.5.4 --- Network Complexity Analysis --- p.127 / Chapter 5. --- Conclusions and Future Work --- p.129 / Publications --- p.132 / Bibliography --- p.133 / Appendix --- p.136

by Tang Kai Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references. / Abstracts in English and Chinese. / Acknowledgements --- p.vi / Abstract --- p.1 / Chapter 1 --- Introduction / Chapter 1.1 --- Motivation --- p.3 / Chapter 1.2 --- Objective --- p.7 / Chapter 1.3 --- Outline --- p.9 / Chapter 2 --- Color Identification / Chapter 2.1 --- Grabbing --- p.11 / Chapter 2.2 --- Digital Image Representation --- p.13 / Chapter 2.3 --- Color Segmentation --- p.15 / Chapter 2.3.1 --- Fuzzy Rule-Based Method --- p.15 / Chapter 2.3.2 --- Fuzzy Clustering Method --- p.20 / Chapter 2.4 --- Conclusion --- p.25 / Chapter 3 --- Shape Recognition / Chapter 3.1 --- Labeling --- p.29 / Chapter 3.1.1 --- Pre-processing --- p.29 / Chapter 3.1.2 --- Connected Components --- p.30 / Chapter 3.2 --- Blob Analysis --- p.33 / Chapter 3.2.1 --- Characteristic Values --- p.33 / Chapter 3.2.2 --- Corner Detection --- p.35 / Chapter 3.3 --- Type Classification --- p.37 / Chapter 3.3.1 --- Standard Blob --- p.37 / Chapter 3.3.2 --- Non-standard Object --- p.39 / Chapter 3.4 --- Flow Chart --- p.39 / Chapter 3.5 --- Point Generation --- p.42 / Chapter 3.5.1 --- Draw the Boundary --- p.42 / Chapter 3.5.2 --- Filling in Color by Lines --- p.48 / Chapter 3.6 --- Conclusion --- p.50 / Chapter 4 --- Drawing / Chapter 4.1 --- Difficulties & Remedies --- p.54 / Chapter 4.1.1 --- Data Transmission Difficulty --- p.54 / Chapter 4.1.2 --- Robot Drawing Plane --- p.56 / Chapter 4.2 --- Coordinates Conversion --- p.59 / Chapter 4.3 --- Quantitative Performance Measure --- p.64 / Chapter 4.4 --- Conclusion --- p.66 / Chapter 5 --- Conclusions & Future Works --- p.69 / Appendix / Bibliography

Kwok, Ka Wai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 113-116). / Abstracts in English and Chinese. / ABSTRACT --- p.i / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1 --- Artistic robot in western art --- p.1 / Chapter 1.2 --- Chinese calligraphy robot --- p.2 / Chapter 1.3 --- Our robot drawing system --- p.3 / Chapter 1.4 --- Thesis outline --- p.3 / Chapter 2. --- ROBOT DRAWING SYSTEM --- p.5 / Chapter 2.1 --- Robot drawing manipulation --- p.5 / Chapter 2.2 --- Input modes --- p.6 / Chapter 2.3 --- Visual-feedback system --- p.8 / Chapter 2.4 --- Footprint study setup --- p.8 / Chapter 2.5 --- Chapter summary --- p.10 / Chapter 3. --- LINE STROKE EXTRACTION AND ORDER ASSIGNMENT --- p.11 / Chapter 3.1 --- Skeleton-based line trajectory generation --- p.12 / Chapter 3.2 --- Line stroke vectorization --- p.15 / Chapter 3.3 --- Skeleton tangential slope evaluation using MIC --- p.16 / Chapter 3.4 --- Skeleton-based vectorization using Bezier curve interpolation --- p.21 / Chapter 3.5 --- Line stroke extraction --- p.25 / Chapter 3.6 --- Line stroke order assignment --- p.30 / Chapter 3.7 --- Chapter summary --- p.33 / Chapter 4. --- PROJECTIVE RECTIFICATION AND VISION-BASED CORRECTION --- p.34 / Chapter 4.1 --- Projective rectification --- p.34 / Chapter 4.2 --- Homography transformation by selected correspondences --- p.35 / Chapter 4.3 --- Homography transformation using GA --- p.39 / Chapter 4.4 --- Visual-based iterative correction example --- p.45 / Chapter 4.5 --- Chapter summary --- p.49 / Chapter 5. --- ITERATIVE ENHANCEMENT ON OFFSET EFFECT AND BRUSH THICKNESS --- p.52 / Chapter 5.1 --- Offset painting effect by Chinese brush pen --- p.52 / Chapter 5.2 --- Iterative robot drawing process --- p.53 / Chapter 5.3 --- Iterative line drawing experimental results --- p.56 / Chapter 5.4 --- Chapter summary --- p.67 / Chapter 6. --- GA-BASED BRUSH STROKE GENERATION --- p.68 / Chapter 6.1 --- Brush trajectory representation --- p.69 / Chapter 6.2 --- Brush stroke modeling --- p.70 / Chapter 6.3 --- Stroke simulation using GA --- p.72 / Chapter 6.4 --- Evolutionary computing results --- p.77 / Chapter 6.5 --- Chapter summary --- p.95 / Chapter 7. --- BRUSH STROKE FOOTPRINT CHARACTERIZATION --- p.96 / Chapter 7.1 --- Footprint video capturing --- p.97 / Chapter 7.2 --- Footprint image property --- p.98 / Chapter 7.3 --- Experimental results --- p.102 / Chapter 7.4 --- Chapter summary --- p.109 / Chapter 8. --- CONCLUSIONS AND FUTURE WORKS --- p.111 / BIBLIOGRAPHY --- p.113

by Fung Wai Keung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 146-151). / Acknowledgements --- p.viii / Abstract --- p.1 / Chapter 1 --- Overview --- p.3 / Chapter 1.1 --- Tactile Sensing in Human and Robot --- p.4 / Chapter 1.1.1 --- Human Hands and Robotic Hands --- p.4 / Chapter 1.1.2 --- Mechanoreceptors in skin and Tactile Sensor Arrays --- p.7 / Chapter 1.2 --- Motivation --- p.12 / Chapter 1.3 --- Objectives --- p.13 / Chapter 1.4 --- Related Work --- p.14 / Chapter 1.4.1 --- Using Vision Alone --- p.15 / Chapter 1.4.2 --- Integration of Vision and Touch --- p.15 / Chapter 1.4.3 --- Using Touch Sensing Alone --- p.17 / Chapter 1.4.3.1 --- Ronald S. Fearing's Work --- p.18 / Chapter 1.4.3.2 --- Peter K. Allen's Work --- p.22 / Chapter 1.5 --- Outline --- p.26 / Chapter 2 --- Geometric Models --- p.27 / Chapter 2.1 --- Introduction --- p.27 / Chapter 2.2 --- Superquadrics --- p.27 / Chapter 2.2.1 --- 2D Superquadrics --- p.27 / Chapter 2.2.2 --- 3D Superquadrics --- p.29 / Chapter 2.3 --- Model Recovery of Superquadric Models --- p.31 / Chapter 2.3.1 --- Problem Formulation --- p.31 / Chapter 2.3.2 --- Least Squares Optimization --- p.33 / Chapter 2.4 --- Free-Form Deformations --- p.34 / Chapter 2.4.1 --- Bernstein Basis --- p.36 / Chapter 2.4.2 --- B-Spline Basis --- p.38 / Chapter 2.5 --- Other Geometric Models --- p.41 / Chapter 2.5.1 --- Generalized Cylinders --- p.41 / Chapter 2.5.2 --- Hyperquadrics --- p.42 / Chapter 2.5.3 --- Polyhedral Models --- p.44 / Chapter 2.5.4 --- Function Representation --- p.45 / Chapter 3 --- Sensing Strategy --- p.54 / Chapter 3.1 --- Introduction --- p.54 / Chapter 3.2 --- Sensing Algorithm --- p.55 / Chapter 3.2.1 --- Assumption of objects --- p.55 / Chapter 3.2.2 --- Haptic Exploration Procedures --- p.56 / Chapter 3.3 --- Contour Tracing --- p.58 / Chapter 3.4 --- Tactile Sensor Data Preprocessing --- p.59 / Chapter 3.4.1 --- Data Transformation and Sensor Calibration --- p.60 / Chapter 3.4.2 --- Noise Filtering --- p.61 / Chapter 3.5 --- Curvature Determination --- p.64 / Chapter 3.6 --- Step Size Determination --- p.73 / Chapter 4 --- 3D Shape Reconstruction --- p.80 / Chapter 4.1 --- Introduction --- p.80 / Chapter 4.2 --- Correspondence Problem --- p.81 / Chapter 4.2.1 --- Affine Invariance Property of B-splines --- p.84 / Chapter 4.2.2 --- Point Inversion Problem --- p.87 / Chapter 4.3 --- Parameter Triple Interpolation --- p.91 / Chapter 4.4 --- 3D Object Shape Reconstruction --- p.94 / Chapter 4.4.1 --- Heuristic Approach --- p.94 / Chapter 4.4.2 --- Closed Contour Recovery --- p.97 / Chapter 4.4.3 --- Control Lattice Recovery --- p.102 / Chapter 5 --- Implementation --- p.105 / Chapter 5.1 --- Introduction --- p.105 / Chapter 5.2 --- Implementation Tool - MATLAB --- p.105 / Chapter 5.2.1 --- Optimization Toolbox --- p.107 / Chapter 5.2.2 --- Splines Toolbox --- p.108 / Chapter 5.3 --- Geometric Model Implementation --- p.109 / Chapter 5.3.1 --- FFD Examples --- p.111 / Chapter 5.4 --- Shape Reconstruction Implementation --- p.112 / Chapter 5.5 --- 3D Model Reconstruction Examples --- p.120 / Chapter 5.5.1 --- Example 1 --- p.120 / Chapter 5.5.2 --- Example 2 --- p.121 / Chapter 6 --- Conclusion --- p.128 / Chapter 6.1 --- Future Work --- p.129 / Appendix --- p.133 / Bibliography --- p.146

This report presents a system for generating a stable, feasible, and reachable grasp of a polyhedral object. A set of contact points on the object is found that can result in a stable grasp; a feasible grasp is found in which the robot contacts the object at those contact points; and a path is constructed from the initial configuration of the robot to the stable, feasible final grasp configuration. The algorithm described in the report is designed for the Salisbury hand mounted on a Puma 560 arm, but a similar approach could be used to develop grasping systems for other robots.

A method is presented for stably grasping 2 dimensional polygonal objects with a dextrous hand when object models are not avaiable. Basic constraints on object vertex angles are found for feasible grasping with two fingers. Local tactile information can be used to determine the finger motion that will reach feasible grasping locations. With an appropriate choice of finger stiffness, a hand can automatically grasp these objects with two fingers. The bounded slip of a part in a hand is shown to be valuable for adapting the fingers and object to a stable situation. Examples are given to show the ability of this grasping method to accomodate disturbance forces and to perform simple part reorientations and regrasping operations.

abstract: Robotic systems are outmatched by the abilities of the human hand to perceive and manipulate the world. Human hands are able to physically interact with the world to perceive, learn, and act to accomplish tasks. Limitations of robotic systems to interact with and manipulate the world diminish their usefulness. In order to advance robot end effectors, specifically artificial hands, rich multimodal tactile sensing is needed. In this work, a multi-articulating, anthropomorphic robot testbed was developed for investigating tactile sensory stimuli during finger-object interactions. The artificial finger is controlled by a tendon-driven remote actuation system that allows for modular control of any tendon-driven end effector and capabilities for both speed and strength. The artificial proprioception system enables direct measurement of joint angles and tendon tensions while temperature, vibration, and skin deformation are provided by a multimodal tactile sensor. Next, attention was focused on real-time artificial perception for decision-making. A robotic system needs to perceive its environment in order to make decisions. Specific actions such as “exploratory procedures” can be employed to classify and characterize object features. Prior work on offline perception was extended to develop an anytime predictive model that returns the probability of having touched a specific feature of an object based on minimally processed sensor data. Developing models for anytime classification of features facilitates real-time action-perception loops. Finally, by combining real-time action-perception with reinforcement learning, a policy was learned to complete a functional contour-following task: closing a deformable ziplock bag. The approach relies only on proprioceptive and localized tactile data. A Contextual Multi-Armed Bandit (C-MAB) reinforcement learning algorithm was implemented to maximize cumulative rewards within a finite time period by balancing exploration versus exploitation of the action space. Performance of the C-MAB learner was compared to a benchmark Q-learner that eventually returns the optimal policy. To assess robustness and generalizability, the learned policy was tested on variations of the original contour-following task. The work presented contributes to the full range of tools necessary to advance the abilities of artificial hands with respect to dexterity, perception, decision-making, and learning. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2016

Robotic grasping and manipulation remains an active area of research despite significant progress over the past decades. Many existing solutions still struggle to robustly handle difficult situations that a robot might encounter even in non-contrived settings.For example, grasping systems struggle when the object is not centrally located in the robot's workspace. Also, grasping in dynamic environments presents a unique set of challenges. A stable and feasible grasp can become infeasible as the object moves; this problem becomes pronounced when there are obstacles in the scene. This research is inspired by the observation that object-manipulation tasks like grasping, pick-and-place or insertion require different forms of awareness. These include reachability awareness -- being aware of regions that can be reached without self-collision or collision with surrounding objects; tactile awareness-- ability to feel and grasp objects just tight enough to prevent slippage or crushing the objects; and 3D awareness -- ability to perceive size and depth in ways that makes object manipulation possible. Humans use these capabilities to achieve a high level of coordination needed for object manipulation. In this work, we develop techniques that equip robots with similar sensitivities towards realizing a reliable and capable home-assistant robot. In this thesis we demonstrate the importance of reasoning about the robot's workspace to enable grasping systems handle more difficult settings such as picking up moving objects while avoiding surrounding obstacles. Our method encodes the notion of reachability and uses it to generate not just stable grasps but ones that are also achievable by the robot. This reachability-aware formulation effectively expands the useable workspace of the robot enabling the robot to pick up objects from difficult-to-reach locations. While recent vision-based grasping systems work reliably well achieving pickup success rate higher than 90\% in cluttered scenes, failure cases due to calibration error, slippage and occlusion were challenging. To address this, we develop a closed-loop tactile-based improvement that uses additional tactile sensing to deal with self-occlusion (a limitation of vision-based system) and adaptively tighten the robot's grip on the object-- making the grasping system tactile-aware and more reliable. This can be used as an add-on to existing grasping systems. This adaptive tactile-based approach demonstrates the effectiveness of closed-loop feedback in the final phase of the grasping process. To achieve closed-loop manipulation all through the manipulation process, we study the value of multi-view camera systems to improve learning-based manipulation systems. Using a multi-view Q-learning formulation, we develop a learned closed-loop manipulation algorithm for precise manipulation tasks that integrates inputs from multiple static RGB cameras to overcome self-occlusion and improve 3D understanding. To conclude, we discuss some opportunities/ directions for future work.

This thesis presents the design, modeling, and development of adaptive robot hands that are capable of performing dexterous, in-hand manipulation. The robot hand comprises of anthropomorphic robotic fingers, which employ an adaptive actuation mechanism. The mechanism achieves both flexion/extension and adduction/abduction, on the finger's metacarpophalangeal joint, by using two actuators. Moment arm pulleys are employed to drive the tendon laterally, such that an amplification on the abduction motion occurs, while also maintaining the flexion motion. Particular emphasis has been given to the modeling and the analysis of the actuation mechanism. Also, a model for spatial motion is provided that relates the actuation modes with the finger motion and the tendon force with the finger characteristics. For the hand design, the use of differential mechanisms simplifies the actuation scheme, as we utilize only two actuators for four fingers, achieving affordable dexterity. A design optimization framework assess the results of hand anthropometry studies to derive key parameters for the bio-inspired actuation design. The model assumptions are evaluated with the finite element method. The proposed finger has been fabricated with the Hybrid Deposition Manufacturing technique and the actuation mechanism's efficiency has been validated with experiments that include the computation of the finger workspace, the assessment of the force exertion capabilities, the demonstration of the feasible motions, and the grasping and manipulation capabilities. Also, the hand design is fabricated with off-the-shelf materials and rapid prototyping techniques while its efficiency has been validated using an extensive set of experimental paradigms that involved the execution of grasping and in-hand manipulation tasks with everyday objects. / Master of Science / This thesis presents the design, modeling, and development of adaptive robot hands that are capable of performing selective interdigitation, robust grasping, and dexterous, in-hand manipulation. The robotic fingers employ an adaptive actuation mechanism. The design is minimal and the hand is capable of performing selective interdigitation, robust grasping, and dexterous, in-hand manipulation. Particular emphasis has been given to the modeling and the analysis of the actuation mechanism. For the hand design, the use of differential mechanisms simplifies the actuation scheme, as we utilize only two actuators for four fingers, achieving affordable dexterity. A design optimization framework assess the results of hand anthropometry studies to derive key parameters for the actuation design. The robotic fingers and the anthropomorphic hand were fabricated using off-the-self materials and additive manufacturing techniques. Several experiments were performed to validate the efficacy of the robot hand.