Grasping, the skill to hold objects and tools while doing in-hand manipulation, still is in many cases an unsolvable problem for robotics, but a natural act for humans. An efficient grasping requires not only human-like robotic hands with articulated fingers but also tactile, force, and kinesthetic sensors for the precise control of the forces and motions exerted during the manipulation. As a fully autonomous robotic dexterous manipulation is too difficult to develop for changing and unstructured environments, an alternative approach is to combine the low-level robot computer control with the higher-level perception and task planning abilities of a human operator equipped with an adequate human-computer interface (HCI). This thesis presents theoretical and experimental contributions to the development of an upgraded haptic-enabled anthropomorphic Ring Ada dexterous robotic hand and a biology-inspired synergistic real-time control system for teleoperated grasping of different objects using a CyberTouch HCI data glove. A fuzzy logic controller module was developed to efficiently control the underactuated Ring Ada' robotic hand during grasping. A machine learning classification system was developed to recognize grasped objects. Experiments have convincingly demonstrated that our novel Ring Ada robotic hand equipped with kinematic position sensors and touch sensors is able to efficiently grasp different lightweight objects through teleoperation.

Robots should offer a human-like level of dexterity when handling objects if humans are to be replaced in dangerous and uncertain working environments. This level of dexterity for human-like manipulation must come from both the hardware, and the control. Exact replication of human-like degrees of freedom in mobility for anthropomorphic robotic hands are seen in bulky, costly, fully actuated solutions, while machine learning to apply some level of human-like dexterity in underacted solutions is unable to be applied to a various array of objects. This thesis presents experimental and theoretical contributions of a novel neuro-fuzzy control method for dextrous human grasping based on grasp synergies using a Human Computer Interface glove and upgraded haptic-enabled anthropomorphic Ring Ada dexterous robotic hand. Experimental results proved the efficiency of the proposed Adaptive Neuro-Fuzzy Inference Systems to grasp objects with high levels of accuracy.

This book looks at the common problems both human and robotic hands encounter when controlling the large number of joints, actuators and sensors required to efficiently perform motor tasks such as object exploration, manipulation and grasping. The authors adopt an integrated approach to explore the control of the hand based on sensorimotor synergies that can be applied in both neuroscience and robotics. Hand synergies are based on goal-directed, combined muscle and kinematic activation leading to a reduction of the dimensionality of the motor and sensory space, presenting a highly effective solution for the fast and simplified design of artificial systems. Presented in two parts, the first part, Neuroscience, provides the theoretical and experimental foundations to describe the synergistic organization of the human hand. The second part, Robotics, Models and Sensing Tools, exploits the framework of hand synergies to better control and design robotic hands and haptic/sensing systems/tools, using a reduced number of control inputs/sensors, with the goal of pushing their effectiveness close to the natural one. Human and Robot Hands provides a valuable reference for students, researchers and designers who are interested in the study and design of the artificial hand.

The human hand and its dexterity in grasping and manipulating objects are some of the hallmarks of the human species. For years, anatomic and biomechanical studies have deepened the understanding of the human hand’s functioning and, in parallel, the robotics community has been working on the design of robotic hands capable of manipulating objects with a performance similar to that of the human hand. However, although many researchers have partially studied various aspects, to date there has been no comprehensive characterization of the human hand’s function for grasping and manipulation of everyday life objects. This monograph explores the hypothesis that the confluence of both scientific fields, the biomechanical study of the human hand and the analysis of robotic manipulation of objects, would greatly benefit and advance both disciplines through simulation. Therefore, in this book, the current knowledge of robotics and biomechanics guides the design and implementation of a simulation framework focused on manipulation interactions that allows the study of the grasp through simulation. As a result, a valuable framework for the study of the grasp, with relevant applications in several fields such as robotics, biomechanics, ergonomics, rehabilitation and medicine, has been made available to these communities.

Robot Hands and Multi-Fingered Haptic Interfaces is a monograph focusing on the comparison of human hands with robot hands, the fundamentals behind designing and creating the latter, and robotics' latest advancements in haptic technology.This work discusses the design of robot hands; contact models at grasping; kinematic models of constraint; dynamic models of the multi-fingered hand; the stability theorem of non-linear control systems; robot hand control; design and control of multi-fingered haptic interfaces; application systems using multi-fingered haptic interfaces; and telecontrol of robot hands using a multi-fingered haptic interface.Robot Hands and Multi-Fingered Haptic Interfaces is intended mainly for readers who have a foundation in basic robot arm engineering. To understand robot hand manipulation, readers must study kinematic constraint models of fingers, hand dynamics with constraints, stability theorems of non-linear control, and multi-fingered hand control -- this book will benefit readers' understanding of this full range of issues regarding robot hand manipulation.

This book presents a new set of devices for accurate investigation of human finger stiffness and force distribution in grasping tasks. The ambitious goal of this research is twofold, the first is to advance the state of the art on human strategies in manipulation tasks and provide tools to assess rehabilitation procedure and the second is to investigate human strategies for impedance control that can be used for human robot interaction and control of myoelectric prosthesis. Part one describes two types of systems that are able to achieve a complete set of measurements on force distribution and contact point locations. The effectiveness of these devices in grasp analysis is also experimentally demonstrated and applications to neuroscientific studies are discussed. In part two, the devices are exploited in two different studies to investigate stiffness regulation principles in humans. The first study provides evidence on the existence of coordinated stiffening patterns in the fingers of human hands and establishes initial steps towards a real-time and effective modelling of finger stiffness in tripod grasp. The second study presents experimental findings on how humans modulate their hand stiffness whilst grasping objects of varying levels of compliance. The overall results give solid evidence on the validity and utility of the proposed devices to investigate human grasp properties. The underlying motor control principles that are exploited by humans in the achievement of a reliable and robust grasp can potentially be integrated into the control framework of robotic or prosthetic hands to achieve a similar interaction performance.

The unpredictability of unstructured environments poses a significant challenge to fully autonomous systems. A human in the loop can provide high-level decisions that leverage experience, context, and human creativity. Allowing humans to operate remotely through teleoperation improves flexibility and safety in hazardous conditions. Our work addresses human-in-the-loop teleoperation applications in assistive robotics and hazardous environments with granular materials. We demonstrate the critical role that haptic feedback plays in enabling humans and robots to operate in unstructured environments effectively. In study #1, we developed a tactile-based proximity sensing framework for estimating the distance to buried objects in granular materials by exploiting granular media jamming. As a sensorized robot finger displaces granular material towards a buried object, jamming granular particles leads to increased fingertip contact forces. A tactile sensor effectively acts as a proximity sensor when moved through granular materials. Using our framework, the distance to a buried object can be estimated using solely haptic feedback. In study #2, we developed a structured approach for estimating the orientation and diameter of cables buried in sand using a deformable, vision-based tactile sensor. Our orientation estimation method identifies the contact region of the cable by employing a proximity function that takes extracted image features as input. Our approach is compared with existing image feature extraction methods, including PCA and maximally stable extremal regions. Our framework demonstrates the effectiveness of tactile sensing when an object is partially or fully occluded in granular materials. In study #3, we designed a telepresence system with real-time auditory, visual, and haptic feedback. The system comprises a modified Stretch mobile manipulator with additional controllable degrees of freedom in the wrist and fingers. The operator interface consists of a virtual reality headset and a haptic glove. The operator controls the remote robot using intuitive controls based on motion tracking. To convey tactile information, the operator receives electrical impulses to stimulate the afferent sensory nerves of each finger driven by tactile feedback from the robot hand. In summary, we developed tactile-based systems and approaches to enhance human-in-the-loop teleoperation for applications in unstructured environments, such as in the home and in granular materials.