Journal Articles by Minas Liarokapis
Automation in Construction, 2023
The use of Unmanned Aerial Vehicles (UAVs) for bridge inspection has gained popularity recently; ... more The use of Unmanned Aerial Vehicles (UAVs) for bridge inspection has gained popularity recently; however, accurately localising the UAV in GPS-denied areas is still challenging, which hinders the development of fully autonomous UAV-assisted bridge inspection solutions. This paper proposes a fiducial marker-corrected stereo visual-inertial localisation (FMC-SVIL) method, running on a resource-constrained onboard computer, to estimate UAV's global pose underneath bridge girders. The proposed FMC-SVIL utilises an optimised stereo visualinertial odometry for continuous relative pose estimation between consecutive camera frames and an improved AprilTag2-based measurement algorithm for accurate global referencing and periodic pose corrections. The method is validated through extensive experiments, and the results show that the FMC-SVIL achieved UAV localisation with a root mean square error of 0.416 m in sunny conditions and 0.340 m in cloudy conditions. FMC-SVIL outperforms the leading vision-based simultaneous localisation and mapping (SLAM) algorithms for flights over multiple bridge spans.
IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
The past decade has seen great progress in the development of adaptive, low-complexity, underactu... more The past decade has seen great progress in the development of adaptive, low-complexity, underactuated robot hands. An advantage of these hands is that they use under-constrained mechanisms and compliance, which facilitate grasping even under significant object pose uncertainties. However, for many minimal contact grasps such as precision fingertip grasps, these hands tend to move the object after a grasp is secured, to an equilibrium configuration determined by the elasticity of the mechanism and the contact forces exerted through the robot fingertips. In this paper, we present a methodology based on constrained optimization methods for deriving stable, minimal effort grasps for underactuated robot hands and compensating for post-contact, in-hand parasitic object motions. To do so, we compute the imposed object motions for different object shapes and sizes and we synthesize appropriate robot arm trajectories that eliminate them. The approach allows for the computation of these grasps and motions even for hands with complex, flexure-based, compliant members. The effectiveness of the proposed methods is validated using a redundant robot arm (Barrett WAM) and a two fingered, compliant, underactuated robot hand (Yale Open Hand model T42), for a series of simulated and experimental paradigms.
IEEE Transactions on Automation Science and Engineering, 2018
In this paper, we focus on the formulation of a hybrid methodology that combines analytical model... more In this paper, we focus on the formulation of a hybrid methodology that combines analytical models, constrained optimization schemes and machine learning techniques to simplify the execution of dexterous, in-hand manipulation tasks with adaptive robot hands. More precisely, the constrained optimization scheme is used to describe the kinematics of adaptive hands during the grasping and manipulation processes, unsupervised learning (clustering) is used to group together similar manipulation strategies, dimensionality reduction is used to either extract a set of representative motion primitives (for the identified groups of manipulation strategies) or to solve the manipulation problem in a low-d space and finally an automated experimental setup is used for unsupervised, automated collection of large datasets. We also assess the capabilities of the derived manipulation models and primitives for both model and everyday life objects, and we analyze the resulting manipulation ranges of motion (e.g., object perturbations achieved during the dexterous, in-hand manipulation). We show that the proposed methods facilitate the execution of fingertip-based, within-hand manipulation tasks while requiring minimal sensory information and control effort, and we demonstrate this experimentally on a range of adaptive hands. Finally, we introduce DexRep, an online repository for dexterous manipulation models that facilitate the execution of complex tasks with adaptive robot hands. Note to Practitioners-Robot grasping and dexterous, in-hand manipulation are typically executed with fully actuated robot hands that rely on analytical methods, computation of the hand object system Jacobians and extensive numerical simulations for deriving optimal strategies. However, these hands require sophisticated sensing elements, complicated control laws and are not robust to external disturbances or perception uncertainties. Recently, a new class of adaptive hands was proposed that uses structural compliance and under-actuation (less motors than the available degrees of freedom) to offer increased robustness and simplicity. In this paper, we propose hybrid methodologies that blend analytical models with constrained optimization schemes and learning techniques to simplify the execution of dexterous, in-hand manipulation tasks with adaptive robot hands.
IEEE Robotics and Automation Letters, 2019
Soft, under-actuated and compliant robotic exo-gloves have received an increased interest over th... more Soft, under-actuated and compliant robotic exo-gloves have received an increased interest over the last decade. Possible applications of these systems range from augmenting the capabilities of healthy individuals to restoring the mobility of people that suffer from paralysis or stroke. Despite the significant progress in the field, most existing solutions are still heavy and expensive, they require an external power source to operate, and they are not wearable. In this paper, we focus on the development of adaptive (underactuated and compliant), tendon-driven, wearable exo-gloves and we propose two compact, affordable and lightweight assistive devices that provide grasping capabilities enhancement to the user. The devices are experimentally tested and their efficiency is validated using three different types of tests: i) grasping tests that involve different everyday objects, ii) force exertion capability tests that assess the fingertip forces that can be exerted while using the exo-gloves, and iii) motion tracking experiments focusing on the finger bending profile. The devices are able to significantly enhance the grasping capabilities of their user with a weight of 335 g and a cost of 92 USD for the body powered version and a weight of 562 g and a cost of 369 USD for the motorized exo-glove version.
IEEE Robotics and Automation Letters, 2019
Soft, lightweight, underactuated assistive gloves (exogloves)
can be useful for enhancing the cap... more Soft, lightweight, underactuated assistive gloves (exogloves)
can be useful for enhancing the capabilities of a healthy
individual or to assist the rehabilitation of patients who suffer from
conditions that limit the mobility of their fingers. However, most
solutions found in the literature do not offer individual control of
the fingers, hindering the execution of different types of grasps. In
this letter, we focus on the development of a soft, underactuated,
tendon-driven exo-glove that is equipped with a muscle-machine
interface combining Electromyography and Forcemyography sensors
to decode the user intent and allow the execution of specific
grasp types. The device is experimentally tested and evaluated using
different types of experiments: first, grasp experiments to assess
the capability of the proposed muscle machine interface to discriminate
between different grasp types and second, force exertion capability
experiments, which evaluate the maximum forces that can
be applied for different grasp types. The proposed device weighs
1150 g and costsā¼1000 USD (in parts). The exoglove is capable of
considerably improving the grasping capabilities of the user, facilitating
the execution of different types of grasps and exerting forces
up to 20 N.
Frontiers in Robotics and AI, 2019
This paper presents an adaptive actuation mechanism that can be employed for the development of a... more This paper presents an adaptive actuation mechanism that can be employed for the development of anthropomorphic, dexterous robot hands. The tendon-driven actuation mechanism achieves both flexion/extension and adduction/abduction on the finger's metacarpophalangeal joint using two actuators. Moment arm pulleys are employed to drive the tendon laterally and achieve a simultaneous execution of abduction and flexion motion. Particular emphasis has been given to the modeling and analysis of the actuation mechanism. More specifically, the analysis determines specific values for the design parameters for desired abduction angles. Also, a model for spatial motion is provided that relates the actuation modes with the finger motions. A static balance analysis is performed for the computation of the tendon force at each joint. A model is employed for the computation of the stiffness of the rotational flexure joints. The proposed mechanism has been designed and fabricated with the hybrid deposition manufacturing technique. The efficiency of the mechanism has been validated with experiments that include the assessment of the role of friction, the computation of the reachable workspace, the assessment of the force exertion capabilities, the demonstration of the feasible motions, and the evaluation of the grasping and manipulation capabilities. An anthropomorphic robot hand equipped with the proposed actuation mechanism was also fabricated to evaluate its performance. The proposed mechanism facilitates the collaboration of actuators to increase the exerted forces, improving hand dexterity and allowing the execution of dexterous manipulation tasks.
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019
Electromyography(EMG) based interfaces are
the most common solutions for the control of robotic,
... more Electromyography(EMG) based interfaces are
the most common solutions for the control of robotic,
orthotic, prosthetic, assistive, and rehabilitation devices,
translatingmyoelectric activations into meaningful actions.
Over the last years, a lot of emphasis has been put into
the EMG based decoding of human intention, but very few
studies have been carried out focusing on the continuous
decoding of human motion. In this work, we present a
learning scheme for the EMG based decoding of object
motions in dexterous, in-hand manipulation tasks. We also
study the contribution of differentmuscleswhile performing
these tasks and the effect of the gender and hand size in the
overall decoding accuracy. To do that, we use EMG signals
derived from 16 muscle sites (8 on the hand and 8 on the
forearm) from 11 different subjects and an optical motion
capture system that records the object motion. The object
motion decoding is formulated as a regression problem
using the Random Forests methodology. Regarding feature
selection, we use the following time-domain features: root
mean square,waveformlength and zero crossings.A10-fold
cross validation procedure is used for model assessment
purposes and the feature variable importance values are
calculated for each feature. This study shows that subject
specific,hand specific,and object specificdecodingmodels
offer better decoding accuracy that the generic models.
Frontiers in Neurorobotics, 2019
Adaptive robot hands are typically created by introducing structural compliance either in
their j... more Adaptive robot hands are typically created by introducing structural compliance either in
their joints (e.g., implementation of flexures joints) or in their finger-pads. In this paper,
we present a series of alternative uses of structural compliance for the development
of simple, adaptive, compliant and/or under-actuated robot grippers and hands that
can efficiently and robustly execute a variety of grasping and dexterous, in-hand
manipulation tasks. The proposed designs utilize only one actuator per finger to
control multiple degrees of freedom and they retain the superior grasping capabilities
of the adaptive grasping mechanisms even under significant object pose or other
environmental uncertainties. More specifically, in this work, we introduce, discuss, and
evaluate: (a) a design of pre-shaped, compliant robot fingers that adapts/conforms to
the object geometry, (b) a hyper-adaptive finger-pad design that maximizes the area of
the contact patches between the hand and the object, maximizing also grasp stability,
and (c) a design that executes compliance adjustable manipulation tasks that can be
predetermined by tuning the in-series compliance of the tendon routing system and
by appropriately selecting the imposed tendon loads. The grippers are experimentally
tested and their efficiency is validated using three different types of tests: (i) grasping
tests that involve different everyday objects, (ii) grasp quality tests that estimate the
contact area between the grippers and the objects grasped, and (iii) dexterous, in-hand
manipulation experiments to evaluate the manipulation capabilities of the Compliance
Adjustable Manipulation (CAM) hand. The devices employ mechanical adaptability to
facilitate and simplify the efficient execution of robust grasping and dexterous, in-hand
manipulation tasks.
IEEE Robotics and Automation Letters, 2020
The human hand is Nature's most versatile and dexterous end-effector and it has been a source of ... more The human hand is Nature's most versatile and dexterous end-effector and it has been a source of inspiration for roboticists for over 50 years. Recently, significant industrial and research effort has been put into the development of dexterous robot hands and grippers. Such end-effectors offer robust grasping and dexterous, in-hand manipulation capabilities that increase the efficiency, precision, and adaptability of the overall robotic platform. This work focuses on the development of modular, sensorized objects that can facilitate benchmarking of the dexterity and performance of hands and grippers. The proposed objects aim to offer; a minimal, sufficiently diverse solution, efficient pose tracking, and accessibility. The object manufacturing instructions, 3D models, and assembly information are made publicly available through the creation of a corresponding repository.
IEEE Access, 2020
Robotic hand exoskeletons have become a popular and efficient technological solution for assistin... more Robotic hand exoskeletons have become a popular and efficient technological solution for assisting people that suffer from neurological conditions and for enhancing the capabilities of healthy individuals. This class of devices ranges from rigid and complex structures to soft, lightweight, wearable gloves. In this work, we propose a hybrid (tendon-driven and pneumatic), lightweight, affordable, easy-to-operate exoskeleton glove equipped with variable stiffness, laminar jamming structures, abduction/adduction capabilities, and a pneumatic telescopic extra thumb that increases grasp stability. The efficiency of the proposed device is experimentally validated through five different types of experiments: i) abduction/adduction tests, ii) force exertion experiments that capture the forces that can be exerted by the proposed device under different conditions, iii) bending profile experiments that evaluate the effect of the laminar jamming structures on the way the fingers bend, iv) grasp quality assessment experiments that focus on the effect of the inflatable thumb on enhancing grasp stability, and v) grasping experiments involving everyday objects and seven subjects. The hybrid assistive, exoskeleton glove considerably improves the grasping capabilities of the user, being able to exert the forces required to execute a plethora of activities of daily living. All files that allow the replication of the device are distributed in an open-source manner.
Frontiers in Robotics and AI, 2020
Traditionally, the robotic end-effectors that are employed in unstructured and dynamic environmen... more Traditionally, the robotic end-effectors that are employed in unstructured and dynamic environments are rigid and their operation requires sophisticated sensing elements and complicated control algorithms in order to handle and manipulate delicate and fragile objects. Over the last decade, considerable research effort has been put into the development of adaptive, under-actuated, soft robots that facilitate robust interactions with dynamic environments. In this paper, we present soft, retractable, pneumatically actuated, telescopic actuators that facilitate the efficient execution of stable grasps involving a plethora of everyday life objects. The efficiency of the proposed actuators is validated by employing them in two different soft and hybrid robotic grippers. The hybrid gripper uses three rigid fingers to accomplish the execution of all the tasks required by a traditional robotic gripper, while three inflatable, telescopic fingers provide soft interaction with objects. This synergistic combination of soft and rigid structures allows the gripper to cage/trap and firmly hold heavy and irregular objects. The second, simplistic and highly affordable robotic gripper employs just the telescopic actuators, exhibiting an adaptive behavior during the execution of stable grasps of fragile and delicate objects. The experiments demonstrate that both grippers can successfully and stably grasp a wide range of objects, being able to exert significantly high contact forces.
IEEE Access, 2021
The increased use of Virtual and Augmented Reality based systems necessitates the development of ... more The increased use of Virtual and Augmented Reality based systems necessitates the development of more intuitive and unobtrusive means of interfacing. Over the last years, Electromyography (EMG) based interfaces have been employed for interaction with robotic and computer applications, but no studies have been carried out to investigate the continuous decoding of the effects of human motion (e.g., manipulated object behavior) in simulated and virtual environments. In this work, we compare the object motion decoding accuracy of an EMG based learning framework for two different dexterous manipulation scenarios: i) for simulated objects handled by a teleoperated model of a hand within a virtual environment and ii) for real, everyday life objects manipulated by the human hand. To do that, we utilize EMG activations from 16 muscle sites (9 on the hand and 7 on the forearm). The object motion decoding is formulated as a regression problem using the Random Forests methodology. A 5-fold cross validation procedure is used for model assessment purposes and the feature variable importance values are calculated for each model. The decoding accuracy for the real world is considerably higher than the virtual world. Each of the objects examined had a single manipulation motion that offered the highest estimation accuracy across both worlds. This study also shows that it is feasible to decode the object motions using just the myoelectric activations of the muscles of the forearm and the hand. This is particularly surprising since simulations lacked haptic feedback and the ability to account for other dynamic phenomena like friction and contact rolling.
Frontiers in Robotics and AI, 2021
Robot grasping in unstructured and dynamic environments is heavily dependent on the object attrib... more Robot grasping in unstructured and dynamic environments is heavily dependent on the object attributes. Although Deep Learning approaches have delivered exceptional performance in robot perception, human perception and reasoning are still superior in processing novel object classes. Furthermore, training such models requires large, difficult to obtain datasets. This work combines crowdsourcing and gamification to leverage human intelligence, enhancing the object recognition and attribute estimation processes of robot grasping. The framework employs an attribute matching system that encodes visual information into an online puzzle game, utilizing the collective intelligence of players to expand the attribute database and react to real-time perception conflicts. The framework is deployed and evaluated in two proof-of-concept applications: enhancing the control of a robotic exoskeleton glove and improving object identification for autonomous robot grasping. In addition, a model for estimating the framework response time is proposed. The obtained results demonstrate that the framework is capable of rapid adaptation to novel object classes, based purely on visual information and human experience.
IEEE Access, 2021
This work proposes a framework that improves the dexterous manipulation capabilities of two finge... more This work proposes a framework that improves the dexterous manipulation capabilities of two fingered grippers by: i) optimizing the finger link dimensions and the interfinger distance for a given object and ii) analyzing the effect of finger symmetry and the distance between the finger base frames on their manipulation workspaces. The results of the workspace analysis motivate the development of a multimodal, adaptive robotic gripper. In particular, the finger link lengths optimization problem is solved by a parallel multi-start search algorithm. The optimal link lengths are then used for the workspace analysis. The results of the analysis demonstrate that different inter-finger distances lead to completely different workspace shapes and that the ratio defined by the area of the optimized workspace (nominator) and the union of all workspaces (denominator), is always significantly less than 1. This means that the area of the union of all workspaces is always larger than the area of the ''optimized'' workspace. Based on these results the proposed robotic gripper is equipped with reconfigurable finger bases that vary the inter-finger distance as well as with selectively lockable robotic finger joints, offering an increased dexterous manipulation performance without sacrificing grasping efficiency. The device is considered multi-modal as it can be used both as a parallel jaw gripper and as an adaptive robotic gripper.
IEEE Transactions on Medical Robotics and Bionics, 2021
Robots use their end-effectors to grasp and manipulate objects in unstructured and dynamic enviro... more Robots use their end-effectors to grasp and manipulate objects in unstructured and dynamic environments. Robot hands and grippers can vary from rigid and complex designs to soft, inflatable, and lightweight structures. In this paper, we focus on the modelling and development of a pneumatically driven soft robotic gripper with retractable telescopic fingers and finger bases with abduction / adduction capabilities. Both the main fingers and the base actuators use a pre-folded, telescopic structure facilitating passive retraction. The efficiency of the proposed device is experimentally validated through different types of experiments: i) grasping experiments that involve different everyday objects ranging from household objects and fragile items to medical waste and consumables, ii) force exertion experiments that capture the maximum forces that can be exerted by the proposed device when utilizing the different actuators of the gripper, and iii) grasp resistance experiments that focus on the effect of the inflatable structure on resisting environmental uncertainties and disturbances. The proposed gripper is able to grasp a plethora of objects, and can exert more than 14 N of grasping force. The design is so low-cost and modular that the soft fingers and palm pad of the gripper can be used in a disposable manner, facilitating the execution of specialized tasks (e.g., grasping in contaminated environments, handling of medical waste, etc). When it is not inflated, the gripper profile is thin and compact to facilitate storage.
IEEE Robotics and Automation Letters, 2021
As manufacturing trends shift towards customized production, the demand for agile automation syst... more As manufacturing trends shift towards customized production, the demand for agile automation systems capable of efficient adaptation to rapidly changing task requirements is rising. This work presents a flexible robotic assembly system that combines CAD based component localization, compliance control, and a multi-modal gripper to enable robust and efficient programming of complex tasks. The process can be easily configured for novel assemblies through a dedicated Graphical User Interface (GUI), which facilitates component identification and task sequencing. Component poses are extracted from 3D CAD models in reference to the assembly origin, while the active compliance scheme compensates for minor positioning errors. The gripper incorporates a parallel jaw element, a rotating module, and an electromagnet to minimize retooling delays. The first iteration of the proposed system placed first in the manufacturing track of the Robotic Grasping and Manipulation Competition of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), experimentally validating its efficiency.
IEEE Access, 2022
Electromyography (EMG) signals are commonly used for the development of Muscle Machine Interfaces... more Electromyography (EMG) signals are commonly used for the development of Muscle Machine Interfaces. EMG-based solutions provide intuitive and often hand-free control in a wide range of applications that range from the decoding of human intention in classification tasks to the continuous decoding of human motion employing regression models. In this work, we compare various machine learning and feature extraction methods for the creation of EMG based control frameworks for dexterous robotic telemanipulation. Various models are needed that can decode dexterous, in-hand manipulation motions and perform hand gesture classification in real-time. Three different machine learning methods and eight different time-domain features were evaluated and compared. The performance of the models was evaluated in terms of accuracy and time required to predict a data sample. The model that presented the best performance and prediction time trade-off was used for executing in real-time a telemanipulation task with the New Dexterity Autonomous Robotic Assistance (ARoA) platform (a humanoid robot). Various experiments have been conducted to experimentally validate the efficiency of the proposed methods. The robotic system is shown to successfully complete a series of tasks autonomously as well as to efficiently execute tasks in a shared control manner.
Frontiers in Robotics and AI, 2022
IEEE transactions on neural systems and rehabilitation engineering, 2022
Electromyography (EMG) signals have been used in designing muscle-machine interfaces (MuMIs) for ... more Electromyography (EMG) signals have been used in designing muscle-machine interfaces (MuMIs) for various applications, ranging from entertainment (EMG controlled games) to human assistance and human augmentation (EMG controlled prostheses and exoskeletons). For this, classical machine learning methods such as Random Forest (RF) models have been used to decode EMG signals. However, these methods depend on several stages of signal pre-processing and extraction of hand-crafted features so as to obtain the desired output. In this work, we propose EMG based frameworks for the decoding of object motions in the execution of dexterous, in-hand manipulation tasks using raw EMG signals input and two novel deep learning (DL) techniques called Temporal Multi-Channel Transformers and Vision Transformers. The results obtained are compared, in terms of accuracy and speed of decoding the motion, with RF-based models and Convolutional Neural Networks as a benchmark. The models are trained for 11 subjects in a motion-object specific and motion-object generic way, using the 10-fold cross-validation procedure. This study shows that the performance of MuMIs can be improved by employing DL-based models with raw myoelectric activations instead of developing DL or classic machine learning models with hand-crafted features.
IEEE Robotics and Automation Letters, 2022
Coaxial rotors used on multirotor Micro Aerial Vehicles (MAVs) are complex aerodynamic systems th... more Coaxial rotors used on multirotor Micro Aerial Vehicles (MAVs) are complex aerodynamic systems that are typically treated in a simplified manner, operating in sub-optimal conditions. In this letter, we propose: i) an open-source benchmarking platform for coaxial rotor systems that allows us to analyse and improve their efficiency and ii) a map of the whole actuation domain of coaxial systems based on extensive experimentation. In particular, we test three systems built using off-the-shelf components and different rotor configurations. Results demonstrate the existence of a maximum efficiency boundary, which covers the whole thrust range of each system. We also analyze how this boundary changes with respect to the rotor configuration. We compare it with the performance of coaxial rotors controlled with the current standard method (i.e., equal commands for both rotors). Finally, we propose a control allocation strategy that improves the efficiency of coaxial rotors by up to 11% over the current industry standard. Implementation on an octocopter with four sets of coaxial rotors validates the proposed methods across two different rotor separation designs and two different payload scenarios.
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Journal Articles by Minas Liarokapis
can be useful for enhancing the capabilities of a healthy
individual or to assist the rehabilitation of patients who suffer from
conditions that limit the mobility of their fingers. However, most
solutions found in the literature do not offer individual control of
the fingers, hindering the execution of different types of grasps. In
this letter, we focus on the development of a soft, underactuated,
tendon-driven exo-glove that is equipped with a muscle-machine
interface combining Electromyography and Forcemyography sensors
to decode the user intent and allow the execution of specific
grasp types. The device is experimentally tested and evaluated using
different types of experiments: first, grasp experiments to assess
the capability of the proposed muscle machine interface to discriminate
between different grasp types and second, force exertion capability
experiments, which evaluate the maximum forces that can
be applied for different grasp types. The proposed device weighs
1150 g and costsā¼1000 USD (in parts). The exoglove is capable of
considerably improving the grasping capabilities of the user, facilitating
the execution of different types of grasps and exerting forces
up to 20 N.
the most common solutions for the control of robotic,
orthotic, prosthetic, assistive, and rehabilitation devices,
translatingmyoelectric activations into meaningful actions.
Over the last years, a lot of emphasis has been put into
the EMG based decoding of human intention, but very few
studies have been carried out focusing on the continuous
decoding of human motion. In this work, we present a
learning scheme for the EMG based decoding of object
motions in dexterous, in-hand manipulation tasks. We also
study the contribution of differentmuscleswhile performing
these tasks and the effect of the gender and hand size in the
overall decoding accuracy. To do that, we use EMG signals
derived from 16 muscle sites (8 on the hand and 8 on the
forearm) from 11 different subjects and an optical motion
capture system that records the object motion. The object
motion decoding is formulated as a regression problem
using the Random Forests methodology. Regarding feature
selection, we use the following time-domain features: root
mean square,waveformlength and zero crossings.A10-fold
cross validation procedure is used for model assessment
purposes and the feature variable importance values are
calculated for each feature. This study shows that subject
specific,hand specific,and object specificdecodingmodels
offer better decoding accuracy that the generic models.
their joints (e.g., implementation of flexures joints) or in their finger-pads. In this paper,
we present a series of alternative uses of structural compliance for the development
of simple, adaptive, compliant and/or under-actuated robot grippers and hands that
can efficiently and robustly execute a variety of grasping and dexterous, in-hand
manipulation tasks. The proposed designs utilize only one actuator per finger to
control multiple degrees of freedom and they retain the superior grasping capabilities
of the adaptive grasping mechanisms even under significant object pose or other
environmental uncertainties. More specifically, in this work, we introduce, discuss, and
evaluate: (a) a design of pre-shaped, compliant robot fingers that adapts/conforms to
the object geometry, (b) a hyper-adaptive finger-pad design that maximizes the area of
the contact patches between the hand and the object, maximizing also grasp stability,
and (c) a design that executes compliance adjustable manipulation tasks that can be
predetermined by tuning the in-series compliance of the tendon routing system and
by appropriately selecting the imposed tendon loads. The grippers are experimentally
tested and their efficiency is validated using three different types of tests: (i) grasping
tests that involve different everyday objects, (ii) grasp quality tests that estimate the
contact area between the grippers and the objects grasped, and (iii) dexterous, in-hand
manipulation experiments to evaluate the manipulation capabilities of the Compliance
Adjustable Manipulation (CAM) hand. The devices employ mechanical adaptability to
facilitate and simplify the efficient execution of robust grasping and dexterous, in-hand
manipulation tasks.