This paper presents a shear-based control scheme for grasping and manipulating delicate objects with a Pisa/IIT anthropomorphic SoftHand equipped with soft biomimetic tactile sensors on all five fingertips. These `microTac' tactile sensors are miniature versions of the TacTip vision-based tactile sensor, and can extract precise contact geometry and force information at each fingertip for use as feedback into a controller to modulate the grasp while a held object is manipulated. Using a parallel processing pipeline, we asynchronously capture tactile images and predict contact pose and force from multiple tactile sensors. Consistent pose and force models across all sensors are developed using supervised deep learning with transfer learning techniques. We then develop a grasp control framework that uses contact force feedback from all fingertip sensors simultaneously, allowing the hand to safely handle delicate objects even under external disturbances. This control framework is applied to several grasp-manipulation experiments: first, retaining a flexible cup in a grasp without crushing it under changes in object weight; second, a pouring task where the center of mass of the cup changes dynamically; and third, a tactile-driven leader-follower task where a human guides a held object. These manipulation tasks demonstrate more human-like dexterity with underactuated robotic hands by using fast reflexive control from tactile sensing.

This paper presents the design, characterization and validation of a wearable haptic device able to convey skin stretch, force feedback, and a combination of both, to the user's arm. In this work, we carried out physical and perceptual characterization with eleven able-bodied participants as well as two experiments of discrimination and manipulation task hiring a total of 32 participants. In both the experiments the CUFF was used in conjunction with the Pisa/IIT SoftHand. The first experiment was a discrimination task where the subjects had to recognize the dimension and the softness between pair of cylinder. in the second experiment the subjects were asked to control the robotic hand for grasping objects. After the experiments the subjects underwent to a subjective evaluation of the device. Results of the experiments and questionnaire showed the effectiveness of the proposed device. Thank to its versatility and structure, the device could be a viable solution for teleoperation application, guidance and rehabilitation tasks, including prosthesis applications.

This paper presents a control scheme for force sensitive, gentle grasping with a Pisa/IIT anthropomorphic SoftHand equipped with a miniaturised version of the TacTip optical tactile sensor on all five fingertips. The tactile sensors provide high-resolution information about a grasp and how the fingers interact with held objects. We first describe a series of hardware developments for performing asynchronous sensor data acquisition and processing, resulting in a fast control loop sufficient for real-time grasp control. We then develop a novel grasp controller that uses tactile feedback from all five fingertip sensors simultaneously to gently and stably grasp 43 objects of varying geometry and stiffness, which is then applied to a human-to-robot handover task. These developments open the door to more advanced manipulation with underactuated hands via fast reflexive control using high-resolution tactile sensing.

This paper introduces the BRL/Pisa/IIT (BPI) SoftHand: a single actuator-driven, low-cost, 3D-printed, tendon-driven, underactuated robot hand that can be used to perform a range of grasping tasks. Based on the adaptive synergies of the Pisa/IIT SoftHand, we design a new joint system and tendon routing to facilitate the inclusion of both soft and adaptive synergies, which helps us balance durability, affordability and grasping performance of the hand. The focus of this work is on the design, simulation, synergies and grasping tests of this SoftHand. The novel phalanges are designed and printed based on linkages, gear pairs and geometric restraint mechanisms, and can be applied to most tendon-driven robotic hands. We show that the robot hand can successfully grasp and lift various target objects and adapt to hold complex geometric shapes, reflecting the successful adoption of the soft and adaptive synergies. We intend to open-source the design of the hand so that it can be built cheaply on a home 3D-printer. For more detail: https://sites.google.com/view/bpi-softhandtactile-group-bri/brlpisaiit-softhand-design

IIT's teams will compete in the "Powered Arm Prosthesis" category showing two different robotic arm prostheses made in Italy: SoftHandPro and Hannes. The race course is about 30 metres long and will see the pilots compete in three races on 6 stations reproducing daily tasks. The IIT-Istituto Italiano di Tecnologia (Italian Institute of Technology) will participate in the Cybathlon Global Edition 2020, an international event organised by the Federal Institute of Technology (ETH) in Zurich, Switzerland, and dedicated to new prosthetic devices. People with physical disabilities from all over the world will compete, as pilots, in different disciplines that reproduce daily useful tasks, using the latest discoveries in technology such as robotic prostheses, exoskeletons and new generation wheelchairs. IIT will participate by presenting two robotic arm prostheses: the SoftHand Pro, stemming from a research project funded by the European Research Council (ERC), and the Hannes robotic hand developed together with the Italian Centro Protesi INAIL (the prosthetic unit of the National Institute for Insurance against Accidents at Work).