Humanoid robotic hands need to be versatile and capable of providing environmental information in order to serve as a platform for intelligent grasp control. To facilitate the design process of such hands, we present the KIT Robotic Hands. They have been designed to meet diverse application requirements through their scalability in size, actuation, sensorization and computing resources. The hands integrate a multi-modal sensor system, in-hand embedded processing capabilities, an adaptive underactuated mechanism and a continuously controllable thumb rotation to enhance dexterity. The flexibility of the design is demonstrated through two application-specific hand implementations: one is the ARMAR-7 hand, which has human hand dimensions for grasping daily objects in household tasks, the other is the ARMAR-DE hand, a larger hand designed for grasping bigger objects in decontamination tasks. We describe the design and mechatronics of the hands as well as an evaluation of the grasp success and image segmentation based on an in-hand integrated camera and onboard processing of visual data.

Grasping with five-fingered humanoid hands is a complex control problem. Throughout the entire grasping motion, all finger joints need to be coordinated to achieve a stable grasp. Grasp synergies provide a simplified, low-dimensional representation of grasp postures and motions, that can be used for the description of human grasps as well as the generation of novel, human-like grasps. However, the abstract synergy representation complicates the association of relevant high-level grasp parameters, as for example the grasp type and final posture or the grasp speed. Therefore, it is difficult to control these grasp characteristics in the synergy space. This paper presents an adaptable representation for kinematic grasping motions in synergy space, that allows the generation of novel, human-like grasps under direct control of high-level grasp parameters. It is based on via-point movement primitives trained on synergy trajectories of human grasping motions. The representation using synergy primitives allows for a straightforward adaptation of grasp characteristics while preserving the essential grasping motion learned from human demonstration. The kinematic synergy primitives have a low reproduction error of 3.9% of the maximum finger joint angle and are able to generate successful grasps on a simulated human hand and a real prosthetic hand.

Humans can intuitively grasp objects of different shape and weight. Throughout the grasp execution they control and coordinate the grasp forces at all contact points between the hand and the object to achieve a stable grasp. Dexterous grasping with humanoid hands relies on the perfect coordination between grasp posture and force balance at the contact points in a high dimensional space and remains a challenge. In this paper, we present temporal force synergies describing the change in human grasp forces during the grasp execution in a low-dimensional space based on two new grasp synergy models: 1) static force synergies that are derived by a Principal Component Analysis and represent temporal grasp forces as a sequence of time-independent synergy configurations and 2) dynamic force synergies that are learned by a recurrent neural network and encode the temporal change of grasp forces throughout grasp execution in a latent synergy space clustered by grasp types. We show that both synergy spaces encode human grasp forces with an error of less than 2% and allow the generation of human-like grasp force patterns. Grasp forces for stable grasps described by the dynamic force synergies achieve a grasp quality comparable to demonstrated human grasps in simulation.

We present a novel underactued humanoid five finger soft hand, the KIT Finger-Vision Soft Hand, which is equipped with cameras in the fingertips and integrates a high performance embedded system for visual processing and control. We describe the actuation mechanism of the hand and the tendon-driven soft finger design with internally routed high-bandwidth flat-flex cables. For efficient on-board parallel processing of visual data from the cameras in each fingertip, we present a hybrid embedded architecture consisting of a field programmable logic array (FPGA) and a microcontroller that allows the realization of visual object segmentation based on convolutional neural networks. We evaluate the hand design by conducting durability experiments with one finger and quantify the grasp performance in terms of grasping force, speed and grasp success. The results show that the hand exhibits a grasp force of 31.8 ± 1.2 N and a mechanical durability of the finger of more than 15.000 closing cycles. Finally, we evaluate the accuracy of visual object segmentation during the different phases of the grasping process using five different objects. Hereby, an accuracy above 90% can be achieved.

The development and control of prosthetic hands is an active research area and recently progress in mechatronics, sensor integration and innovative control has been made. However, integration of different components into a prosthetic hand remains challenging due to space constraints, the requirements regarding holistic integration and the need for a user interface. In this paper, we present the KIT prosthetic hand, a novel five-finger 3D printed hand prosthesis, with its underactuated mechanism, sensors and embedded control system. The hand mechanics is based on the underactuated TUAT/Karlsruhe mechanism with two motors actuating 10 degrees of freedom. The mechanism has been realized in 3D printing technologies to facilitate a personalization of the prosthetic hand in terms of size and kinematic parameters. The prosthesis has been designed as a 50thpercentile male hand. It integrates an advanced embedded system as well as an RGB camera in the base of the palm and a colour display in the back of the hand. Experiments indicate a finger tip force of 7.48 N to 11.82 N, a hook grasp force of 120 N and a hand closing time of ~ 1.3 s.

Tendon-driven continuum robots possess versatile application capabilities and have a robust design. The actuation of such robots with non-straight tendons that wrap around the backbone, described by a variable function, offers a lot of untapped potentials. While it has been shown that these continuum robots are able to take up complex shapes using only one actuated segment, the merits of non-straight tendon routing have not been quantified in terms of workspace and motion. In this paper, we show that one additional helically routed tendon can greatly benefit the robot's reachable workspace. For instance, the reachable workspace of a one-segment robot with 3 conventional straight tendons increases by 400 % by adding one helically routed tendon. Furthermore, the dexterity of such a continuum robot is improved, i.e. motion sequences to avoid obstacles or to twine an object for grasping. For the first time, the potential of tendon-driven continuum robots with two segments and helically routed tendons is investigated. The general findings on the merits of helical tendon routing are supported with both simulation and experimental results.