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 work in progress, innovative practice paper proposes a Learning and Teaching Impact Assessment Tool (TALIAT) that would help in assessing the sways of Generative Artificial Intelligence (GAI) on important aspects of Learning and Teaching (L&T). The focus of TALIAT is to identify the most relevant elements of GAI grammar and establish how they would reflect on Learning and Teaching practices. GAIsupported systems are generally perceived beneficial as they ensure resource availability, quick and ongoing feedback, higher level of interactivity, and continued engagement. GAI tools also inculcate the ability to perform independent research through progressive and systematic probing and consequent delivery of information. Nonetheless, issues pertaining to trust, liability, transparency, honesty, fairness in judgement and trustworthy reporting of student success need to be addressed for proper adoption of GAI applications. It is being acknowledged now that various stakeholders like system developers, administrators, policymakers, psychologists, educators, sociologists, and legal experts need to work together and agree upon standards and frameworks for producing professionally, ethically, and legally acceptable GAI systems. For these stakeholders to work together, an easy to follow and adopt assessment tool needs to be designed. This would facilitate establishing methods, and processes for confidently determining impacts of GAI technologies on Learning and Teaching. The current literature has yet to provide such facilitation. Such enablement would: improve teacher's perception of, and users' confidence in, GAI systems as tools like TALIAT would assist in assessing impacts of GAI tools prior to their deployment. The proposed TALIAT could also safeguard quality of information, student competence reporting, academic culture, and social and ethical values. TALIAT projects sixteen elements of grammar on seven important aspects of GAI implications namely, handling biases, prompt tuning and evaluation, output fairness, regulatory compliance, error detection, accountability and explainability. These seven aspects are deemed important for investigating the purpose, impetuses, scope, and L&T impact mitigation strategies of GAI applications. A set of thirty-two questions is included in TALIAT for GAI tools' impact assessment. TALIAT will be useful for the GAI stakeholders to work together as it enables informed decision making about adopting GAI systems and places rail guards for development of trustworthy GAI systems.

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.

In this work, we report on how a sense of touch can be used to control an underactuated anthropomorphic robot hand, based on an integration that respects the hand’s mechanical functionality. Our focus is on integrating the sensorimotor control of the Pisa/IIT SoftHand, an anthropomorphic soft robot hand designed around the principle of adaptive synergies, with the BRL tactile fingertip (TacTip), a soft biomimetic optical tactile sensor. We consider: (i) closed-loop tactile control to establish a light contact on an unknown held object, based on the structural similarity of the tactile image; and (ii) controlling the estimated pose of a held object, using a convolutional neural network approach developed for other TacTip sensors. Accurate control was found for a range of hard and soft objects (to sub-millimetre accuracy and a few degrees). Overall, this gives a foundation to endow soft robotic hands with human-like touch, with implications for autonomous grasping, manipulation, human-robot interaction and prosthetics.