Intrinsic Tactile Sensing (ITS) is a well-established technique, relying on force/torque and geometric surface description to find contact centroids. The method works well for rigid surfaces. However, finding a solution for deformable surfaces is an open issue. This work presents two solutions to extend ITS to deformable surfaces, relying on force-deformation characteristics of the surface under exploration: (i) a closed-form approach that calculates the contact centroid using standard ITS, but on a shrunk geometry approximating the deformed surface; (ii) an iterative procedure that takes into account soft surface deformation, and force/torque equilibrium to minimize a cost function. We have tested both using ellipsoid silicone specimens, with different softness levels and indented along different directions. Both linear and quadratic fitting for the force-indentation behavior were employed. The two methods have distinct advantages and limitations. However, a combination of two methods, using one to produce the initial guess for the other, turns out to be very effective. Indeed, in our validation this solution showed convergence under 1ms, attaining errors lower than 1 mm. The proposed approaches were implemented in a ROS-based toolbox, integrating both solutions.

Recently, the avenue of adaptable, soft robotic hands has opened simplified opportunities to grasp different items; however, the potential of soft end effectors (SEEs) is still largely unexplored, especially in human-robot interaction. In this paper, we propose, for the first time, a simple touch-based approach to endow a SEE with autonomous grasp sensory-motor primitives, in response to an item passed to the robot by a human (human-to-robot handover). We capitalize on human inspiration and minimalistic sensing, while hand adaptability is exploited to generalize grasp response to different objects. We consider the Pisa/IIT SoftHand (SH), an under-actuated soft anthropomorphic robotic hand, which is mounted on a robotic arm and equipped with Inertial Measurement Units (IMUs) on the fingertips. These sensors detect the accelerations arisen from contact with external items. In response to a contact, the hand pose and closure are planned for grasping, by executing arm motions with hand closure commands. We generate these motions from human wrist poses acquired from a human maneuvering the SH to grasp an object from a table. We obtained 86% of successful grasps, considering many objects passed to the SH in different manners. We also tested our techniques in preliminary experiments, where the robot moved to autonomously grasp objects from a surface. Results are positive and open interesting perspectives for soft robotic manipulation.

Grasp and manipulation is a complex task, deceivingly simple to accomplish for humans in everyday life, yet challenging to implement in a robotic hand. There is a trend in literature to use information obtained from studies on human grasp for the design and control of robotic manipulators. However, the effectiveness of such approach is dependent on the measurement tools that are available for use with human hands. While there are many sensing solutions that are designed for this purpose, obtaining a complete set of measurements of forces during grasp interaction is still challenging. In this work we aim to bridge this gap by introducing ExoSense, a passive hand exoskeleton. This device can provide position and orientation of the fingertips and, when integrated with the fingertip wearable force/torque sensing system ThimbleSense, a complete characterization of manipulation in terms of generalized forces and position of contacts on each fingertip in a completely wearable and unconstrained manner. After validating the device in terms of end-effector posture measurements and overall accuracy of grasp measurements, we report on a preliminary experiment aiming to show the potentialities of the system to study human internal grasp force variations and for neuroscientific investigation in general.

An effective robotic wrist represents a key enabling element in robotic manipulation, especially in prosthetics. In this paper, we propose an under-actuated wrist system, which is also adaptable and allows to implement different under-actuation schemes. Our approach leverages upon the idea of soft synergies — in particular the design method of adaptive synergies — as it derives from the field of robot hand design. First we introduce the design principle and its implementation and function in a configurable test bench prototype, which can be used to demonstrate the feasibility of our idea. Furthermore, we report on results from preliminary experiments with humans, aiming to identify the most probable wrist pose during the pre-grasp phase in activities of daily living. Based on these outcomes, we calibrate our wrist prototype accordingly and demonstrate its effectiveness to accomplish grasping and manipulation tasks.

In this work, to guarantee the Pisa/IIT SoftHand's grasp robustness against slippage, three reflex control modes, namely Current, Pose and Impedance, are implemented and experimentally evaluated. Towards this objective, ThimbleSense fingertip sensors are designed and integrated into the thumb and middle fingers of the SoftHand for real-time detection and control of the slippage. Current reflex regulates the restoring grasp forces of the hand by modulating the motor's current profile according to an update law. Pose and Impedance reflex modes instead replicate this behaviour by implementing an impedance control scheme. The difference between the two latter is that the stiffness gain in Impedance reflex mode is being varied in addition to the hand pose, as a function of the slippage on the fingertips. Experimental setup also includes a seven degrees-of-freedom robotic arm to realize consistent trajectories (e.g. lifting) among three control modes for the sake of comparison. Different test objects are considered to evaluate the efficacy of the proposed reflex modes in our experimental setup. Results suggest that task-appropriate restoring forces can be achieved using Impedance reflex due to its capability in demonstrating instantaneous and rather smooth reflexive behaviour during slippage. Preliminary experiments on five healthy human subjects provide evidence on the similarity of the control concepts exploited by the humans and the one realized by the Impedance reflex, highlighting its potential in prosthetic applications.

Haptic interfaces are special robots that interact with people to convey touch-related information. In addition to such a discriminative aspect, touch is also a highly emotion-related sense. However, while a lot of effort has been spent to investigate the perceptual mechanisms of discriminative touch and to suitably replicate them through haptic systems in human robot interaction (HRI), there is still a lot of work to do in order to take into account also the emotional aspects of tactual experience (i.e., the so-called affective haptics), for a more naturalistic human-robot communication. In this paper, we report evidences on how a haptic device designed to convey caress-like stimuli can influence physiological measures related to the autonomous nervous system (ANS), which is intimately connected to evoked emotions in humans. Specifically, a discriminant role of electrodermal response and heart rate variability can be associated to two different caressing velocities, which can also be linked to two different levels of pleasantness. Finally, we discuss how the results from this study could be profitably employed and generalized to pave the path towards a novel generation of robotic devices for HRI.

Measuring contact forces applied by a hand to a grasped object is a necessary step to understand the mysteries that still hide in the unparalleled human grasping ability. Nevertheless, simultaneous collection of information about the position of contacts and about the magnitude and direction of forces is still an elusive task. In this paper we introduce a wearable device that addresses this problem, and can be used to measure generalized forces during grasping. By assembling two supports around a commercial 6-axis force/torque sensor we obtain a thimble that can be easily positioned on a fingertip. The device is used in conjunction with an active marker-based motion capture system to simultaneously obtain absolute position and orientation of the thimbles, without requiring any assumptions on the kinematics of the hand. Finally, using the contact centroid algorithm, introduced in [1], position of contact points during grasping are determined. This paper shows the design and implementation of the device, as well as some preliminary experimental validation.