Grasping and manipulation are two of the most important hand functions that allow people to efficiently execute activities of daily living. Over the last years, many robotic devices have been proposed to assist people who suffer from neurological conditions by enhancing their grasping capabilities. In this work, we focus on the development of a robotic exoskeleton glove that can increase the grasp stability and the force exertion capabilities of the user by employing soft, telescopic, inflatable structures on the palmar side of the hand. Also, the proposed design employs a camera and an object identification system to facilitate the development of a shared control scheme that simplifies the operation of the device. The experiments demonstrate that the soft robotic exoskeleton glove can successfully execute semi-autonomous grasps and that the soft telescopic structures can increase the total exerted grasping forces by more than 40% when inflated.

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.

Design optimization can lead to the development of robotic end-effectors with optimal grasping and dexterous, in-hand manipulation capabilities. In particular, the finger link dimensions have been identified as one of the primary design parameters that affects the performance of a robotic gripper. The ability of a gripper to manipulate objects is mainly attributed to the interaction between a set of coordinated fingers. This coordination is primarily affected by the inter-finger distance. This paper presents a framework for finding an appropriate distance between the finger bases of a two-fingered robotic gripper so as to increase the dexterous manipulation workspace for a range of object sizes. To do that, a parallel multi-start search algorithm is employed to solve a multiparametric optimization problem. The results demonstrate that different 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 a multi-modal robotic gripper with movable finger bases was developed. The proposed gripper can vary the distance between the finger bases online and it offers an increased dexterous manipulation workspace without sacrificing grasping performance.

The ongoing global healthcare crisis has amplified the need for automation of manual tasks in several industries and service sectors. Simple household tasks such as tidying and cleaning are in high demand, with only a few robotic platforms capable of performing them due to the mobility, workspace, and dexterity requirements. This work presents ARoA, an autonomous robotic assistant that can execute complex tasks in industrial, service, and home environments. It is equipped with two lightweight, compliant, 7 degree of freedom arms and a pair of adaptive end-effectors that enable efficient execution of a wide range of tasks. Due to the linear rail based torso system that supports the arms, the ARoA offers exceptional flexibility in terms of reachable workspace. A framework for vision-based execution of tidying and cleaning tasks is also proposed and integrated in the platform. The efficiency of the ARoA platform was experimentally validated through two everyday life applications: i) picking up and tidying randomly scattered household objects and ii) cleaning of common surfaces.

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.

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%.

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.

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.