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Recognising and classifying human hand gestures is important for effective communication between humans and machines in applications such as human-robot interaction, human to robot skill transfer, and control of prosthetic devices. Although there are already many interfaces that enable decoding of the intention and action of humans, they are either bulky or they rely on techniques that need careful positioning of the sensors, causing inconvenience when the system needs to be used in real-life scenarios and environments. Moreover, electromyography (EMG), which is the most commonly used technique, captures EMG signals that have a nonlinear relationship with the human intention and motion. In this work, we present lightmyography (LMG) a new muscle machine interfacing method for decoding human intention and motion. Lightmyography utilizes light propagation through elastic media and the change of light luminosity to detect silicone deformation. Lightmyography is similar to forcemyography in the sense that they both record muscular contractions through skin displacements. In order to experimentally validate the efficiency of the proposed method, we designed an interface consisting of five LMG sensors to perform gesture classification experiments. Using this device, we were able to accurately detect a series of different hand postures and gestures. We also compared LMG data with processed EMG data.

With an increasing number of robotic and prosthetic devices, there is a need for intuitive Muscle-Machine Interfaces (MuMIs) that allow the user to have an embodied interaction with the devices they are controlling. Such MuMIs can be developed using machine learning based methods that utilize myoelectric activations from the muscles of the user to decode their intention. However, the choice of the learning method is subjective and depends on the features extracted from the raw Electromyography signals as well as on the intended application. In this work, we compare the performance of five machine learning methods and eight time-domain feature extraction techniques in discriminating between different gestures executed by the user of an EMG based MuMI. From the results, it can be seen that the Willison Amplitude performs consistently better for all the machine learning methods compared in this study, while the Zero Crossings achieves the worst results for the Decision Trees and the Random Forests and the Variance offers the worst performance for all the other learning methods. The Random Forests method is shown to achieve the best results in terms of achieved accuracies (has the lowest variance between subjects). In order to experimentally validate the efficiency of the Random Forest classifier and the Willison Amplitude technique, a series of gestures were decoded in a real-time manner from the myoelectric activations of the operator and they were used to control a robot hand.

Currently, ~ 1.5 million American deaf-blind individuals depend on the availability of interpreting services to communicate in their primary conversational language, tactile American Sign Language (ASL). In an effort to give the deaf-blind community access to a device that facilitates independent communication using tactile ASL, we developed TATUM (Tactile ASL Translational User Mechanism). TATUM employs 15 degrees of actuation in a hand-wrist system that is capable of signing the 26-letter ASL alphabet. Leveraging Interpres, an independent cloud-based service, all servo sequences that render desired fingerspelled letters and ASL words are stored in a web application programming interface (API). A validation study including both deaf and deaf-blind participants confirmed that the TATUM hand mimics a human hand both in size and feel. The current design of TATUM attained an average recognition rate of 94.7% in visual validation, indicative of the potential to support deaf and deaf-blind individuals in communicating via visual and tactile ASL.

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.

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 costs $\sim$ 1000 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.

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 data sets. 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 manipulations 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 which uses structural compliance and underactuation (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.

Soft, underactuated, 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 letter, 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: First, grasping tests that involve different everyday objects, second, force exertion capability tests that assess the fingertip forces that can be exerted while using the exo-gloves, and third, 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 for the body powered version and a weight of 562 g and a cost of $369 for the motorized exo-glove version.

Lecturers of Engineering courses around the world are struggling to increase the engagement of students through the introduction of appropriate hands-on activities and assignments. In Biomechatronics and Robotics courses these assignments typically focus on how certain devices are designed, modelled, fabricated, or controlled. The hardware for these assignments is usually purchased by some external vendor and the students only get the chance to analyze it or program it, so as to execute a useful task (e.g., programming mobile robots to perform path following tasks). Student engagement can be increased by instructing the students to prepare the hardware for their assignment. This also increases the sense of ownership of the project outcomes. In this paper, we present how a robotic gripper / hand design project and the introduction of a grasping and manipulation competition as a course assignment, can significantly increase the student engagement and their understanding of the taught concepts. The presented best practices have been trialed over the last four years in two different courses (one undergraduate and one postgraduate) of the Department of Mechanical Engineering at the University of Auckland in New Zealand. For the particular assignment the students were asked to fully develop a robotic gripper or hand from scratch using a single actuator (only the actuator and the power electronics were provided). The performance of the developed devices was assessed through the participation in a grasping and manipulation competition. All the details of the proposed assignment are presented, hoping that they could help other lecturers and teachers to prepare similar activities.

Electromyography (EMG) is a wearable, noninvasive, commonly used method for measuring the human muscular activations from the surface of the skin. In this work, we present a pilot study that focuses on the formulation of a shared control framework to facilitate the simplified execution of Electromyography (EMG) based telemanipulation tasks with a robotic platform. The framework combines a Random Forests (RF) regression method with a compliance controller that relies on the force measurements collected with a force-torque sensor. The RF regression efficiently maps the myoelectric activations of the human muscles to corresponding human wrist positions. Then, a teleoperation process is used to control the robot arm end-effector’s position, utilizing the human wrist position estimations. The examined application involves semi-autonomous cleaning of a whiteboard surface with the proposed framework. The compliance controller guarantees that a desired contact force will always be maintained on the whiteboard surface during task execution. This ensures that any EMG based decoding inaccuracies will not drive the robot away from the cleaning plane. Essentially, the system projects the EMG based estimation on the cleaning plane. The shared control framework offers robust performance, with minimal training and calibration required.

Robot grasping and manipulation planning in unstructured and dynamic environments is heavily dependent on the attributes of manipulated objects. Although deep learning approaches have delivered exceptional performance in robot perception, human perception and reasoning are still superior in processing novel object classes. Moreover, training such models requires large datasets that are generally expensive to obtain. This work combines crowdsourcing and gamification to leverage human intelligence, enhancing the object recognition and attribute estimation aspects of robot perception. The framework employs an attribute matching system that encodes visual information into an online puzzle game, utilizing the collective intelligence of players to expand an initial attribute database and react to real-time perception conflicts. The framework is deployed and evaluated in a proof-of-concept application for enhancing object recognition in autonomous robot grasping and a model for estimating the response time is proposed. The obtained results demonstrate that given enough players, the framework can offer near real-time labeling of novel objects, based purely on visual information and human experience.

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.

Many studies have focused on Virtual Reality (VR) frameworks for remotely controlling robotic systems. Although VR systems have been used to teleoperate robots in simple scenarios, their effectiveness in terms of accuracy, speed, and usability has not been rigorously evaluated for complex tasks that require accurate trajectories. In this work, an Enhanced Virtual Reality (EVR) framework for robotic teleoperation is evaluated to assess if it can be efficiently used in complex tasks that require accurate control of the robotic end-effector. The environment and the employed robot are captured using RGB-D cameras, while the remote user controls the motion of the robot with VR controllers. The captured data are transmitted and reconstructed in 3D so as to allow the remote user to monitor the task execution progress in real time, using a VR headset. The EVR system is compared with two other interface alternatives: i) teleoperation in pure VR (the model of the robot is rendered with respect to its real joint states), and ii) teleoperation in EVRR (the model of the robot is superimposed on the real robot). The results show that pure point cloud interfaces suffer from visualization issues, reducing the effectiveness of the robot teleoperation. However, the accuracy and user experience can be greatly improved by including the robot model.

Despite the plethora of studies focusing on the design and development of dexterous robotic grippers and hands, researchers have not been able to quantify dexterity due to the lack of well defined measures or tests. Human dexterity on the other hand, is generally measured by employing various hand function tests. This work is inspired from these tests, proposing a new modular, affordable, accessible test for evaluating the grasping and manipulation capabilities of robotic end effectors. More precisely, the test allows for a quantification of the end-effectors' ability to perform various tasks effectively and irrespectively of the individual design parameters. The test rig proposed in this study combines the features of multiple dexterity tests originally developed for humans. The test quantifies dexterity through a weighted sum of task execution success, speed, and accuracy components that results to a dexterity score ranging from 0 (simplistic, nondexterous system) to 1 (human-like system). It should also be noted that dexterity can be evaluated assessing the efficiency of either the robotic hardware or the robotic perception system.

Over the last decades, the development of aerial robots for monitoring, grasping, and package delivery applications has gained momentum in both research and industry. However, grasping capable platforms are currently limited by the payload that they can carry and their operation time, due to the weight of the grasping mechanisms, their high energy consumption, and battery limitations. In this paper, we propose two reconfigurable, lightweight, robust grasping mechanisms that can be used in aerial robotic vehicles offering them grasping capabilities. The two solutions are: i) an ultra-lightweight, net-based design that utilises twisted string actuation to pull together eight 3D printed cylinders that are connected to a net, conforming around the object and ii) a reconfigurable, slider-based grasping mechanism that works like a supersized parallel jaw gripper, employing 3D-printed pads that slide on two carbon fibre shafts with linear bearings. The second design can also be configured as a shape-shifting, grasping capable device that can act as a flying gripper. The efficiency of the proposed grasping mechanisms has been experimentally validated with a series of experiments focusing on grasping of everyday objects and assessing their force exertion capabilities and durability.

Unstructured and uncertain environments that are encountered in search and rescue applications, require complex interactions that involve a wide range of grasps. These grasps are needed in order to handle unpredictable types of objects of various shapes, stiffnesses, and dimensions. Traditionally, the robotic end-effectors used in such situations are rigid and their operation requires sophisticated sensing elements and complicated control algorithms to manipulate delicate and fragile objects. Over the last decade, considerable research effort has been put into the development of adaptive, underactuated, and soft robots that facilitate robust interactions with dynamic environments. In this paper, we propose a three-fingered robotic gripper that combines pneumatic telescopic mechanisms and rigid claws. The gripper uses three large, rigid fingers to accomplish the execution of all the tasks required by a traditional robot 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 experiments demonstrate that the gripper can successfully grasp a plethora of objects exerting up to 60 N of clench force.

Unmanned Aerial Vehicles (UAV) are typically based on rotorcraft designs and have been extensively used to perform mechanical interaction tasks that could involve inspection as well as search-and-rescue operations in critical unstructured and dynamic environments. UAV based aerial manipulation has also received an increased interest and it has attracted considerable research effort in the robotics community. However, the state-of-the-art control approaches that are being applied to traditional underactuated UAVs show critical stability issues during the execution of manipulation tasks. This is due to the fact that these tasks require the exertion of significant forces with respect to the vehicle-weight ratio. Designs of fully-actuated and overactuated systems show better suitability for such tasks but are still specific to certain functions and tasks, lacking the needed agility and flexibility. In this paper, we propose a novel design of an agile, coaxial, omnidirectional rotor module that can move and exert forces in all directions, without limitations on orientation and wiring. This independent rotor unit presents high mobility and maneuverability, high fight autonomy, and offers high potential for all terrain robotic manipulation applications. In this work, the proposed rotor module has been employed to develop an unconventional, hybrid, all terrain robotic rotorcraft that can act both as a drone and as a mobile robot.

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 development of a soft, retractable, telescopic gripper that exhibits an adaptive behaviour that allows efficient and stable grasps with a wide range of objects. The efficiency of the proposed device is experimentally validated through three different types of experiments: i) grasping experiments that involve different everyday objects, ii) force exertion experiments that capture the maximum forces that can be exerted by the proposed device, 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 everyday life objects and it can exert more than 8 N of grasping force. The design is so low-cost that the soft fingers can be used in a disposable manner, facilitating the execution of specialized tasks (e.g., grasping in sterilized environments, handling of medical waste, etc).

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.

Although soft robots are a good alternative to rigid, traditional robots due to their intrinsic compliance and environmental adaptability, there are several drawbacks that limit their impact, such as low force exertion capability and low resistance to deformation. For this reason, soft structures of variable stiffness have become a popular solution in the field to combine the benefits of both soft and rigid designs. In this paper, we develop laminar jamming flexure joints that facilitate the development of adaptive robot grippers with variable stiffness. Initially, we propose a mathematical model of the laminar jamming structures. Then, the model is experimentally validated through bending tests using different materials, pressures, and number of layers. Finally, the soft, laminar jamming structured are employed to develop variable stiffness flexure joints for two different adaptive robot grippers. Bending profile analysis and grasping tests have demonstrated the benefits of the proposed jamming structures and the capabilities of the designed grippers.

Robots and intelligent industrial systems that focus on sorting or inspection of products require end-effectors that can grasp and manipulate the objects surrounding them. The capability of such systems largely depends on their ability to efficiently identify the objects and estimate the forces exerted on them. This paper presents an underactuated, compliant, and lightweight hyper-adaptive robot gripper that can efficiently discriminate between different everyday life objects and estimate the contact forces exerted on them during a single grasp, using vision-based techniques. The hyper-adaptive mechanism consists of an array of movable steel rods that get reconfigured conforming to the geometry of the grasped object. The proposed object identification and force estimation techniques are model-free and do not rely on time consuming object exploration. A series of experiments have been carried out to discriminate between 12 different everyday life objects and estimate the forces exerted on a dynamometer. During each grasp, a series of images are captured that detect the reconfiguration of the hyper-adaptive grasping mechanism. These images are then used by an image processing algorithm to extract the required information about the gripper reconfiguration, classify the object grasped using a Random Forests (RF) classifier, and estimate the amount of force being exerted. The employed RF classifier gives a prediction accuracy of 100%, while the results of the force estimation techniques (Neural Networks, Random Forests, and 3rd order polynomial) range from 94.7% to 99.1%.

To facilitate the development of a new generation of Virtual Reality systems and their introduction in everyday life applications, new intuitive, immersive methods of interfacing have to be developed. Over the years, Electromyography (EMG) based interfaces have been utilized for unobtrusive interaction with computer systems. However, previous EMG studies have not explored the continuous decoding of the effects of human motion (e.g., manipulated object behavior) in simulated and virtual environments. In this work, we present an EMG based learning framework that can allow for an immersive interaction with Virtual Reality environments. To do that, EMG activations from the muscles of the forearm and the hand were acquired during the execution of object manipulation tasks in a virtual world along with the motion of the object. The virtual world was visualized using an HTC Vive VR headset, while the hand motions were tracked with a dataglove equipped with magnetic motion capture sensors. The object motion decoding was formulated as a regression problem using the Random Forests methodology. The study shows that the object motion can be successfully decoded using the EMG activations, despite the lack of haptic feedback.

With the emergence of agile manufacturing in highly automated industrial environments, the demand for efficient robot adaptation to dynamic task requirements is increasing. For assembly tasks in particular, classic robot programming methods tend to be rather time intensive. Thus, effectively responding to rapid production changes requires faster and more intuitive robot teaching approaches. This work focuses on combining programming by demonstration with path optimization and local replanning methods to allow for fast and intuitive programming of assembly tasks that requires minimal user expertise. Two demonstration approaches have been developed and integrated in the framework, one that relies on human to robot motion mapping (teleoperation based approach) and a kinesthetic teaching method. The two approaches have been compared with the classic, pendant based teaching. The framework optimizes the demonstrated robot trajectories with respect to the detected obstacle space and the provided task specifications and goals. The framework has also been designed to employ a local replanning scheme that adjusts the optimized robot path based on online feedback from the camera-based perception system, ensuring collision-free navigation and the execution of critical assembly motions. The efficiency of the methods has been validated through a series of experiments involving the execution of assembly tasks. Extensive comparisons of the different demonstration methods have been performed and the approaches have been evaluated in terms of teaching time, ease of use, and path length.

Electromyography (EMG) based interfaces have been used in various robotics studies ranging from teleoperation and telemanipulation applications to the EMG based control of prosthetic, assistive, or robotic rehabilitation devices. But most of these studies have focused on the decoding of user’s motion or on the control of the robotic devices in the execution of simple tasks (e.g., grasping tasks). In this work, we present a learning scheme that employs High Density Electromyography (HD-EMG) sensors to decode a set of dexterous, in-hand manipulation motions (in the object space) based on the myoelectric activations of human forearm and hand muscles. To do that, the subjects were asked to perform roll, pitch, and yaw motions manipulating two different cubes. The first cube was designed to have a center of mass coinciding with the geometric center of the cube, while for the second cube the center of mass was shifted 14 mm to the right (off-centered design). Regarding the acquisition of the myoelectric data, custom HD-EMG electrode arrays were designed and fabricated. Using these arrays, a total of 89 EMG signals were extracted. The object motion decoding was formulated as a regression problem using the Random Forests (RF) technique and the muscle importances were studied using the inherent feature variables importance calculation procedure of the RF. The muscle importance results show that different subjects use different strategies to execute the same motions on same object when the weight is off-centered. Finally, the decoded motions were used to control a five fingered robotic hand in a proof-of-concept application.

Over the last years, hand exoskeletons have become a popular and efficient technical solution for assisting people that suffer from neurological and musculoskeletal diseases and enhance the capabilities of healthy individuals. These devices can vary from rigid and complex structures to soft, lightweight, wearable gloves. Despite the significant progress in the field, most existing solutions do not provide the same dexterity as the healthy human hand. In this paper, we focus on the development of a hybrid (tendon-driven and pneumatic), lightweight, affordable, wearable exoskeleton glove equipped with abduction/adduction capabilities and a pneumatic telescopic extra thumb that increases grasp stability. The efficiency of the proposed device is experimentally validated through three different types of experiments: i) abduction/adduction tests, ii) force exertion experiments that capture the maximum forces that can be applied by the proposed device, and iii) grasp quality assessment experiments that focus on the effect of the inflatable thumb on enhancing grasp stability. The hybrid assistive glove considerably improves the grasping capabilities of the user, being able to exert the forces required to assist people in the execution of activities of daily living.

In this paper, we present a highly adaptive, 3-fingered, soft robotic gripper with a reasonably low weight (657 g) that is capable of grasping various everyday life objects. The employed soft, pneumatically actuated robotic fingers are able to grasp objects of different shapes without damaging them, and the extendable, soft, telescopic palm is able to absorb the high impact forces of the grasped objects, protecting the on-board sensor and providing adaptation to the object shape. The gripper is tendon-driven and it employs a quick-release mechanism that allows grasping of objects at high speeds. The deformability of the soft robotic fingers and the telescopic palm is evaluated by tracking their motions with an appropriate motion capture system. The proposed gripper facilitates the execution of smooth pre-contact motions. The high working volume of each finger allows for grasping of objects with a variety of shapes and sizes. The behaviour of the gripper and its performance are experimentally validated with grasping experiments that involve a plethora of everyday life objects.

The emerging discussion about the sustainability potential of distributed production is the starting point for this paper. The focus is on the “design global, manufacture local” model. This model builds on the conjunction of the digital commons of knowledge and design with desktop and benchtop manufacturing technologies (from three-dimensional printers and laser cutters to low-tech tools and crafts). Two case studies are presented to illustrate three interlocked practices of this model for degrowth. It is argued that a “design global, manufacture local” model, as exemplified by these case studies, seems to arise in a significantly different political economy from that of the conventional industrial model of mass production. “Design global, manufacture local” may be seen as a platform to bridge digital and knowledge commons with existing physical infrastructures and degrowth communities, in order to achieve distributed modes of collaborative production.

In this paper, we present a methodology based on constrained optimization methods for estimating and compensating for post-contact parasitic object motions for underactuated, compliant robot hands and for deriving stable, minimal effort grasps to try to minimize these movements. To do so, we compute the object motions for different hand designs, object shapes, and object sizes and we synthesize appropriate robot arm trajectories that eliminate them, even in hands with complex flexure-based compliant members. The effectiveness of the proposed methods is validated using a seven DOF robot arm (Barrett WAM) and a range of compliant underactuated robot hands (Yale OpenHand models T42PP, T42PF, and T42FF). Note to Practitioners —During precision fingertip grasps, adaptive hands tend to move the object upon contact, to an equilibrium configuration determined by the elasticity of the mechanism and the forces exerted on the hand-object system. Such a behavior may be undesired for certain tasks (e.g., when grasping a full glass of water, the in-hand object perturbations may spill the water). In this paper, we propose a methodology for adaptive hands that derives stable minimal effort grasps, computes the post-contact parasitic object motions, and eliminates them using compensatory motions of a robot arm.

In this work, a Mixed Reality (MR) system is evaluated to assess whether it can be efficiently used in teleoperation tasks that require an accurate control of the robot end-effector. The robot and its local environment are captured using multiple RGB-D cameras, and a remote user controls the robot arm motion through Virtual Reality (VR) controllers. The captured data is streamed through the network and reconstructed in 3D, allowing the remote user to monitor the state of execution in real time through a VR headset. We compared our method with two other interfaces: i) teleoperation in pure VR, with the robot model rendered with the real joint states, and ii) teleoperation in MR, with the rendered model of the robot superimposed on the actual point cloud data. Preliminary results indicate that the virtual robot visualization is better than the pure point cloud for accurate teleoperation of a robot arm.

The concept of teleoperation has been studied since the advent of robotics and has found use in a wide range of applications, including exploration of remote or dangerous environments (e.g., space missions, disaster management), telepresence based time optimisation (e.g., remote surgery) and robot learning. While a significant amount of research has been invested into the field, intricate manipulation tasks still remain challenging from the user perspective due to control complexity. In this paper, we propose an intuitive, affordances oriented telemanipulation framework for a dual robot arm hand system. An object recognition module is utilised to extract scene information and provide grasping and manipulation assistance to the user, simplifying the control of adaptive, multi-fingered hands through a commercial Virtual Reality (VR) interface. The system’s performance was experimentally validated in a remote operation setting, where the user successfully performed a set of bimanual manipulation tasks.

Current grasping methods for aerial vehicles are slow, inaccurate and they cannot adapt to any target object. Thus, they do not allow for on-the-fly, ultra-fast grasping. In this paper, we present a passive closing, adaptive robot hand design that offers ultra-fast, aerial grasping for a wide range of everyday objects. We investigate alternative uses of structural compliance for the development of simple, adaptive robot grippers and hands and we propose an appropriate quick release mechanism that facilitates an instantaneous grasping execution. The quick release mechanism is triggered by a simple distance sensor. The proposed hand utilizes only two actuators to control multiple degrees of freedom over three fingers and it retains the superior grasping capabilities of adaptive grasping mechanisms, even under significant object pose or other environmental uncertainties. The hand achieves a grasping time of 96 ms, a maximum grasping force of 56 N and it is able to secure objects of various shapes at high speeds. The proposed hand can serve as the end-effector of grasping capable Unmanned Aerial Vehicle (UAV) platforms and it can offer perching capabilities, facilitating autonomous docking.

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.

Adaptive, underactuated and compliant robot hands have received an increased interest over the last decade. Possible applications of these systems range from the development of simple grippers for industrial automation to the creation of anthropomorphic devices that can be used as prosthetic hands. These hands are particularly capable of extracting stable grasps even under significant object pose or other environmental uncertainties, due to the underactuation and the structural compliance of their designs. Despite the increased interest and the promising performance, adaptive hands suffer from several disadvantages and drawbacks. For example, the use of underactuation can lead to a post-contact reconfiguration of the fingers that compromises the force exertion capabilities of the system during pinch grasping. In this paper, we focus on the design, modelling, development, and evaluation of an adaptive robot gripper that uses magnets to adjust the reconfiguration profile of the fingers. The effect of the magnets increases the gripper’s force exertion capabilities in pinch grasps, without compromising the full / caging grasps. The efficiency of the proposed gripper is experimentally validated through two different tests: i) a contact force test that compares the results of a theoretical model with the actual experimental results and ii) a grasping test that assesses the force exertion capabilities and the reconfiguration behaviour of the adaptive fingers for different implementations of the magnetic joints.

Teleoperation and telemanipulation have since the early years of robotics found use in a wide range of applications, including exploration, maintenance, and response in remote or hazardous environments, healthcare, and education settings. As the capabilities of robot manipulators grow, so does the control complexity and the remote execution of intricate manipulation tasks still remains challenging for the user. This paper proposes an intuitive telemanipulation framework based on electromyography (EMG) and fiducial marker based tracking that can be used with a dexterous robot arm hand system. The EMG subsystem captures the myoelectric activations of the user during the execution of specific hand postures and gestures and translates them into the desired grasp type for the robot hand. The pose of the tracked fiducial marker is used as a taskspace goal for the robot end-effector. The system performance is experimentally validated in a remote operation setting, where the system successfully performs a telemanipulation task.

Adaptive, underactuated, and compliant robot hands offer a promising alternative to the fully-actuated, rigid robotic devices that are typically considered for the execution of complex tasks that require significant dexterity. The increasing popularity of adaptive hands is due to their ability to extract stable grasps even under significant object pose or other environmental uncertainties, their lightweight and affordable designs and their intuitiveness and easiness of operation. Regarding possible applications, adaptive hands have been successfully used for the execution of both robust grasping and dexterous, in-hand manipulation tasks. However, the particular class of hands also suffers from certain shortcomings and drawbacks. For example, the use of underactuation leads to a post-contact reconfiguration of the fingers that may affect the force exertion capabilities of the hands during pinch grasping. In this paper, we focus on methods to predict the contact forces exerted by adaptive hands in pinch grasps, using their postcontact reconfiguration profile. The bending profiles of the fingers are recorded using ArUco trackers and IMU sensors that are embedded on the adaptive fingers and which are used to train appropriate regression models. More precisely, we examine the efficiency of the machine learning technique (Random Forests) in predicting the exerted contact forces during the reconfiguration phase of an adaptive finger. The accuracy of the proposed method is experimentally validated for a wide range of conditions, involving different prepositionings of the robot finger with respect to the employed force sensor.

Adaptive robot hands are typically created by introducing structural compliance either in their joints (e.g., implementation of flexure 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) the concept of compliance adjustable motions that can be predetermined by tuning the in-series compliance of the tendon routing system and by appropriately selecting the imposed tendon loads, b) a design paradigm of pre-shaped, compliant robot fingers that adapt / conform to the object geometry and, c) a hyper-adaptive finger-pad design that maximizes the area of the contact patches between the hand and the object, maximizing also grasp stability. The proposed hands use mechanical adaptability to facilitate and simplify the efficient execution of robust grasping and dexterous, in-hand manipulation tasks by design.

Electromyography (EMG) based interfaces are the most common solutions for the control of robotic, orthotic, prosthetic, assistive, and rehabilitation devices, translating myoelectric 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 different muscles while 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, waveform length and zero crossings. A 10-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 specific decoding models offer better decoding accuracy that the generic models.

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. University of Auckland, Faculty of Engineering, FRDF project [3716482]; Office of Naval Research (ONR)Office of Naval Research [N00014-15-1-2125]; National Science FoundationNational Science Foundation (NSF) [NSF CAREER CPS-1851588] This work was supported in part by the University of Auckland, Faculty of Engineering, FRDF project 3716482, in part by the Office of Naval Research (ONR) under Grant N00014-15-1-2125, and in part by the National Science Foundation under grant NSF CAREER CPS-1851588.

Adaptive, tendon-driven and affordable prosthetic devices have received an increased interest over the last decades. Prosthetic devices range from body-powered solutions to fully actuated systems. Despite the significant progress in the field, most existing solutions are expensive, heavy, and bulky, or they cannot be used for partial hand amputations. In this paper, we focus on the development of adaptive, tendon-driven, glove-based, affordable prostheses for partial hand amputations and we propose two compact and lightweight devices (a body powered and a motor driven version). The efficiency of the devices is experimentally validated and their performance is evaluated using two different types of tests: i) grasping tests that involve different everyday objects and ii) tests that assess the force exertion capabilities of the proposed prostheses.

Soft, underactuated, and wearable robotic exo-gloves have received an increased interest over the last years. These devices can be used to improve the capabilities of healthy individuals or to assist people that suffer from neurological and musculoskeletal diseases. 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 an affordable, underactuated, tendon-driven, wearable exo-glove equipped with a novel four-output differential mechanism that provides grasping capabilities enhancement to the user. The device and the differential mechanism are experimentally tested and assessed using three different types of experiments: i) grasping tests that involve different everyday objects, ii) force exertion capability tests that assess the fingertip forces for different types of grasps, and iii) tendon tension tests that estimate the maximum tendon tension that can be obtained by employing the proposed differential. The device considerably improves the grasping capabilities of the user with a weight of 690 g and an operation autonomy of a whole day.

This paper presents a compliant, underactuated finger for the development of anthropomorphic robotic and prosthetic hands. The finger achieves both flexion/extension and adduction/abduction on the metacarpophalangeal joint, by using two actuators. The design employs moment arm pulleys to drive the tendon laterally and amplify the abduction motion, while also maintaining the flexion motion. Particular emphasis has been given to the analysis of the mechanism. 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 reachable workspace, the assessment of the exerted forces at the fingertip, the demonstration of the feasible motions, and the presentation of the grasping and manipulation capabilities. The proposed mechanism facilitates the collaboration of the two actuators to increase the exerted finger forces. Moreover, the extended workspace allows the execution of dexterous manipulation tasks.

Autonomous intelligent systems outperform human workers in an expanding range of domains, typically those in which success is a function of speed, precision and repeatability. However, many cognitive tasks remain beyond the reach of automation. In this work, we propose the use of video games to crowdsource the cognitive versatility and creativity of human players to solve complex problems in industrial automation and robotics applications. To do so, we introduce a theoretical framework in which robotics problems are embedded into video game environments and gameplay from crowds of players is aggregated to inform robot actions. Such a framework could enable a future of synergistic human-machine collaboration for industrial automation, in which members of the public not only freely offer the fruits of their intelligent reasoning for productive use, but have fun whilst doing so. There is also potential for significant negative consequences surrounding safety, accountability and ethics if great care is not taken in the implementation. Further work is needed to explore these wider implications, as well as to develop the technical theory behind the framework and build prototype applications.