This work introduces the New Dexterity modular, dexterous, anthropomorphic, open-source, bimanual manipulation platform (OpenBMP) that is designed for research and rapid experimentation in robot grasping, dexterous manipulation, and bimanual manipulation. The platform combines adaptive and hybrid actuation systems with lockable joints, facilitating transitions between the execution of delicate and forceful tasks. Antagonistic tendon-driven elbows and inline actuator transmissions reduce the system’s inertial mass while enhancing energy efficiency and overall performance. Leveraging 3D printing and carbon fiber reinforced manufacturing of core parts, the platform is easy to replicate and highly modular. This paper presents the details of the design, the actuation principles, and the experimental validation of the efficiency of the platform with the execution of complex teleoperation and telemanipulation tasks. The designs, the electronics, and the code are open-sourced to allow replication by others.

Robots have predominantly been used in automating tasks in structured industrial environments, however, with the advances in technology they are starting to take part in roles in dynamic everyday life scenarios. As a result, the tasks executed by robotic systems will also grow in sophistication. Grasping and dexterous manipulation are critical aspects that allow humans to execute these sophisticated tasks, enabling them to interact with their environment. As such, emulating the human hand can be advantageous for interacting with a world designed for humans. However, directly replicating the anatomical structure of the hand produces designs that are fully actuated, expensive, and which require sophisticated controls and sensing to operate efficiently. In this paper, we present two different versions of the New Dexterity adaptive, humanlike robot hand that is capable of executing robust caging grasps under a wide range of environmental uncertainties (e.g., object pose uncertainties). One of the versions has a classic, fixed thumb base while the second one incorporates an additional degree of freedom at the thumb base, which enables a translational motion for repositioning the thumb and adjusting the aperture. This design choice enhances the inhand manipulation capabilities of the robot hand, improving also the power grasping capabilities for larger objects. The performances of the proposed robot hand designs are experimentally validated and compared through three different tests: i) grasping experiments involving everyday-life objects, ii) force experiments that evaluate their force exertion capabilities, and iii) in-hand manipulation experiments that demonstrate and compare their dexterity.

The advent of collaborative industrial and house-hold robotics has blurred the demarcation between the human and robot workspace. The capability of robots to function efficiently alongside humans requires new research to be conducted in dynamic environments as opposed to the traditional well-structured laboratory. In this work, we propose an efficient skill transfer methodology comprising intuitive interfaces, efficient optical tracking systems, and compliant control of robotic arm-hand systems. The lightweight wearable interfaces mounted with robotic grippers and hands allow the execution of dexterous activities in dynamic environments without restricting human dexterity. The fiducial and reflective markers mounted on the interfaces facilitate the extraction of positional and rotational information allowing efficient trajectory tracking. As the tasks are performed using the mounted grippers and hands, gripper state information can be directly transferred. The hardware-agnostic nature and efficiency of the proposed interfaces and skill transfer methodology are demonstrated through the execution of complex tasks that require increased dexterity, writing and drawing.

To communicate, the ~ 1.5 million Americans living with deafblindess use tactile American Sign Language (t-ASL). To provide Deafßilind (DB) individuals with a means of using their primary communication language without the use of an interpreter, we developed an assistive technology that promotes their autonomy. The TATUM (Tactile ASL Translational User Mechanism) anthropomorphic arm hand system leverages previous developments of a fingerspelling hand to sign more complex ASL words and phrases. The TATUM hand-wrist system is attached onto a 4 DOF robot arm and a human motion recognition and human to robot gesture transfer framework is used for signing recognition and replication. In particular, signing trajectories based on vision-based motion capture data from a sign demonstrator were used to control the robot's actuators. The performance of the system was evaluated through tactile based sign recognition performed by a blinded user and for its accuracy with novice, sighted users.

Soft robots are capable of effortlessly adapting to their environment using elastic materials that impart structural compliance into their designs, allowing them to execute complex tasks with minimal sensing and control. However, soft robots cannot exert high forces and can only handle low deformation forces. These characteristics typically limit their applicabil-ity to tasks that require delicate interactions. In this work, we present a mechanically programmable, variable stiffness, jamming actuator based on an articulated mesh structure. The proposed actuator can elastically bend when it is not activated but compresses to attain a pre-programmed shape that is determined by the mesh geometry of the multi-layer jamming architecture when pressure is applied to the silicone pouch containing it. Unlike traditional jamming structures the utilisation of the articulated mesh structure facilitates elastic deformations past the yield point when jammed. The actuator can become >27 times stiffer than its relaxed configuration when exposed to only 90 kPa pressure. We demonstrate the efficiency of this actuator by developing variable stiffness joints that can be used to create: i) underactuated, tendon driven robotic grippers and soft, disposable robotic grippers that exhibit increased dexterity and ii) wearable, affordable, lightweight elbow exoskeleton systems that can assist humans in holding heavy objects with minimal effort.

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.

Robot grasping and dexterous, in-hand manipulation allow robots to interact with their surroundings and execute a plethora of complex tasks such as pushing buttons, opening doors, and interacting with electrical appliances. In robotics, such complicated tasks are typically executed by multi-fingered end-effectors that are heavy, rigid, and expensive, employing numerous degrees of freedom and actuation. In this paper, we focus on the analysis, design, and development of a multi-grasp, reconfigurable, five fingered, anthropomorphic robot hand that can facilitate the execution of both robust grasping and dexterous manipulation tasks in service robotics and industrial automation applications. The robot hand is composed of eight actuators driving eighteen degrees of freedom with a telescoping mechanism and opposable thumb and pinky fingers to produce multiple anthropomorphic and non-anthropomorphic configurations for grasping and manipulation tasks. The reconfigurable finger base frames allow the hand to transform and utilize its degrees of actuation in an optimal manner to overcome its underactuated limitations. The underactuated robot hand is designed with a human hand structure that takes advantage of objects specifically designed for human operation (e.g., tool or handles with ergonomics for the human hand). This allows the system to better operate within a human-centered environment. The effectiveness of the proposed device is experimentally validated through three different tests: i) grasping experiments involving everyday-life objects, ii) force experiments that assess the force exertion capabilities of the hand in different finger base frame configurations, and iii) demonstration of in-hand object manipulation capabilities. The proposed hand weighs 1.28 kg and has a cost of approximately $1920 USD. The device is capable of exerting up to 14.3 N of contact force during pinch grasping and a maximum of 150.6 N power grasping.

Over the last decade, adaptive tendon driven devices have gained an increased interest from the research community for their lightweight, compact, and affordable design features attributed to the utilisation of underactuation, differential mechanisms, and structural compliance. Although adaptive tendon driven devices are capable of efficiently executing stable grasps under significant object pose uncertainties with simplistic control algorithms, they lack the controllability over individual fingers in comparison to traditional fully actuated designs. In this paper, we focus on the development of a selectively lockable differential mechanism that is powered through a small and low torque servo to provide increased autonomy to highly underactuated and adaptive prosthetic hands, without compromising the weight, cost, and compactness of the device. The proposed prosthetic hand is experimentally validated through four tests: i) grasping posture and gesture execution experiments, ii) grasping experiments with everyday life objects, iii) force exertion experiments, and iv) Electromyography (EMG) based control of the prosthetic hand.

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.

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.

Current trends in industrial automation favor agile systems that allow adaptation to rapidly changing task requirements and facilitate customized production in smaller batches. This work presents a flexible manufacturing system relying on compliance control, CAD based localization, and a multi-modal gripper to enable fast and efficient task programming for assembly operations. CAD file processing is employed to extract component pose data from 3D assembly models, while the system's active compliance compensates for errors in calibration or positioning. To minimize retooling delays, a novel gripper design incorporating both a parallel jaw element and a rotating module is proposed. The developed system placed first in the manufacturing track of the Robotic Grasping and Manipulation Competition of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019, experimentally validating its efficiency.

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.

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.

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.