Over the last decade, a plethora of soft robotic devices have been proposed for the execution of complex grasping and dexterous manipulation tasks. Tasks requiring such increased dexterity are typically executed using fully-actuated, rigid end-effectors equipped with sophisticated sensing and controlled with complex control laws. The new class of soft robotic devices offers an alternative to the traditional end-effectors and facilitates the development of robotic grasping and manipulation solutions that are lightweight, safe to interact with, affordable, and easy to use and control. Within the class of soft robotic grippers and hands, promising recent developments were made in ultra-affordable, even disposable mechanisms based on origami and kirigami structures. This paper proposes a new kirigami-inspired robotic gripper geometry employing compression-based actuation. The compression actuation fundamentally differentiates this new design class from previous kirigami grippers, resulting in more compact robotic grippers with superior grasping capabilities. In particular, we investigate how the shapes of the internal cuts of the kirigami geometries can affect the gripper performance in terms of force exertion and grasping capabilities. A series of experiments are conducted to understand better the working principles behind this new type of kirigami grippers and experimentally validate their efficacy in the execution of complex, everyday life tasks. Further demonstrations of the gripper's capabilities include the pick-and-placing of human hair, egg yolk, and even liquids.

A popular solution for connecting different components in modern electronics, such as mobile phones, laptops, tablets, etc, is the use of flexible flat cables (FFC). Typically, it takes hours of repetition from a highly trained worker, or a high precision autonomous robot with specialised end effectors to reliably manage the installation of these cables. Human workers are prone to error, and cannot work endlessly without a break, while the robots often come with a significant expense, and require a substantial amount of time to program and reprogram. Additionally, the use of sophisticated sensing elements further increases the complexity of the required control system. As a result, the performance and robustness of such systems is far from sufficient, hindering their mass adoption. The manipulation of FFCs is also quite challenging. In this work, we focus on the robotic manipulation of a plethora of flexible cables, proposing a multi-modal gripper with locally-dexterous tips and active fingernails. The fingers of the gripper are equipped with: i) locally-dexterous fingertips that accommodate manipulation-capable degrees of freedom, ii) a combination of Nitinol-based active fingernails and suction cups that allow picking up and handling of cables that rest on flat surfaces, and iii) compliant finger-pads that conform to the object surface to increase grasping stability. The proposed robotic gripper is equipped with a camera and a perception system that allow for the execution of complex cable manipulation and assembly tasks in dynamic environments.

Grasping and manipulation are complex and demanding tasks, especially when executed in dynamic and unstructured environments. Typically, such tasks are executed by rigid articulated end-effectors, with a plethora of actuators that need sophisticated sensing and complex control laws to execute them efficiently. Soft robotics offers an alternative that allows for simplified execution of these demanding tasks, enabling the creation of robust, efficient, lightweight, and affordable solutions that are easy to control and operate. In this work, we introduce a new class of soft, kirigami-based robotic grippers, we study their post-contact behavior, and we investigate different cut patterns for their development. We follow an experimental approach in which several designs are proposed and employed in a series of grasping and force exertion tests to compare their capabilities and post-contact behavior. The results of such experiments indicate a clear relationship between degree of reconfiguration and grasping force, and provide key insights into the effect of the cut patterns in the performance of the designs. These findings are then used in the design process of an improved version of multi-layer, disposable kirigami grippers that are fabricated employing simple 3D printed layers and silicone rubber using the concept of Hybrid Deposition Manufacturing (HDM). A series of experimental results demonstrate that the proposed design and manufacturing methods can enable the creation of soft, kirigami-based grippers with superior grasping capabilities that can handle delicate, contaminated, and everyday life objects and can even be disposed off in an automated way (e.g., after handling hazardous materials, such as medical waste).

For the development of muscle-machine interfaces (MuMIs), researchers have relied mainly on Electromyography (EMG) signals. However, these signals require complex hardware systems, as well as specialized signal processing and feature extraction methods. To overcome these issues, in our previous work, we proposed a novel MuMI for decoding human intention and motion, called Lightmyography (LMG). To improve the performance of this interface even further, in this work, we employ two novel deep learning techniques called Temporal Multi-Channel Transformer (TMC-T) and Temporal Multi-Channel Vision Transformer (TMC-ViT) for the classification of hand gestures based on the LMG data. The performance of these two Transformer-based methods is evaluated and compared with other well-known deep learning and classical machine learning methods. This work also addresses the influence of varying parameters defined during the training phase of decoding models, such as the size and shape of the input data packet. A series of data augmentation techniques were also employed to generate synthetic data and increase the dataset size so as to train deep learning models more efficiently.

This work presents an adaptive prosthetic training gripper with a compact, variable stiffness differential mechanism and a vision-based shared control scheme that relies on a Lightmyography (LMG) interface to trigger the selected grasps. The gripper incorporates three monolithic adaptive fingers manufactured using the concept of Hybrid Deposition Manufacturing (HDM) and includes a gear drive system that allows two of the finger bases to rotate, implementing abduction / adduction and thereby increasing the available grasping workspace. The fingers are actuated through a compact, series-elastic differential mechanism that reduces the total number of required actuators to only two. The developed adaptive robotic gripper is operated using a vision-based myoelectric control framework that utilizes an RGB camera and a Convolutional Neural Network (CNN) for object detection and classification as well as for grasp selection and an LMG muscle machine interface for grasp triggering. The efficiency of the proposed gripper and the control framework have been experimentally validated through a series of complex grasping experiments executed using a plethora of eveyday life objects.

Differential mechanisms allow the designers of robotic and prosthetic grippers and hands to create devices that require a minimal number of motors in order to grasp a plethora of everyday life objects, leading to light-weight, compact, and low-cost implementations. The working principle of differential mechanisms is simple. They allow the distribution of the forces exerted by a single actuator to multiple outputs (e.g., fingers). This reduction in the number of motors leads to underactuation, which is the use of fewer motors than the available degrees of freedom. But differentials need also to be power-efficient, compact, adaptive, and lightweight. Most of the existing solutions lack at least one of these attributes. In this paper, we focus on the design, modeling, and development of a compact, adaptive, series elastic differential. The proposed mechanism consists of four elastic elements connected in series with the four output attachments. The compression of the elastic elements during grasping allows the gripper or hand to conform to the object’s shape. The efficiency of the differential mechanism is experimentally validated using two different types of experiments, measuring: i) the maximum achievable tension load at the outputs, and ii) the maximum achievable compliance of a single output when all other outputs are blocked. The proposed differential has been employed for the development of a gripper and its efficiency has been assessed by executing grasping tasks with several everyday life objects. The device can be easily replicated using additive manufacturing and off-the-shelf materials and is disseminated in an open-source manner.