Food handling is a challenging task for robotic grippers, as it requires to manipulate highly deformable and fragile items, that can be easily damaged. Moreover, ingredients for the preparation of the different dishes are usually stored in small containers that are often not easily accessible. This paper introduces an innovative soft-rigid, tendon-driven gripper: the Double-Scoop Gripper (DSG). Its two-fingered design exploits a specialized structure to cope with constrained spaces (e.g., containers in narrow shelves). The DSG can delicately grasp objects of various shapes by employing two scoop-shaped fingertips that can form a single plate when fingers are flexed. Data obtained from an on-board camera are used to detect the food item features and plan the grasping strategy that better exploits the possible environmental constraints regulating the opening of the two fingers and the approaching direction of the gripper. DSG capabilities are verified with experiments conducted using real food ingredients within a pick-and-place setup to evaluate both the grasping and the releasing capability of the gripper. Obtained results are promising and suggest that this approach could be particularly advantageous in the context of automated food serving.

Multi-robot systems have gained increasing interest across various fields such as medicine, environmental monitoring, and more. Despite the evident advantages, the coordination of the swarm arises significant challenges for human operators, particularly concerning the cognitive burden needed for efficiently controlling the robots. In this study, we present a novel approach for enabling a human operator to effectively control the motion of multiple robots. Leveraging a shared control data-driven approach, we enable a single user to control the 9 degrees of freedom related to the pose and shape of a swarm. Our methodology was evaluated through an experimental campaign conducted in simulated 3D environments featuring a narrow cylindrical path, which could represent, e.g., blood vessels, industrial pipes. Subjective measures of cognitive load were assessed using a post-experiment questionnaire, comparing different levels of autonomy of the system. Results show substantial reductions in operator cognitive load when compared to conventional teleoperation techniques, accompanied by enhancements in task performance, including reduced completion times and fewer instances of contact with obstacles. This research underscores the efficacy of our approach in enhancing human-robot interaction and improving operational efficiency in multi-robot systems.

Augmented reality (AR) is considered one of the most promising solutions for safer procedures in several surgical specialities. Fusing patient-specific pre-operative information, typically 3D models extracted from CT scans or MRI, with real-time surgical images allows the surgeon to have detailed information on the anatomical structure of the surgical target intra-operatively. The coupling of AR and Robotics represents the next step towards introducing awareness into the surgical room, thus enhancing the surgeon's perceptual, cognitive and manipulative capabilities. This paper presents a novel integrated system for real-time AR navigation in robotic minimally invasive surgery (RMIS), composed of a robotic endoscopic camera, a robotic teleoperation implementing a software-based Remote Center of Motion (RCM), and an AR navigation software based on an initial manual registration of virtual 3D models with the real anatomy. The integrated system, as well as the individual modules, were evaluated in simulated surgical-like setups for accuracy and repeatability. The proposed system can perform high-precision tasks (position accuracy around$1 mm$and AR error lower than 7%), showing potential for application in different surgical procedures and setting the basis for autonomous robotic surgery operations.

Finding effective grasp strategies constitutes one of the main challenges in robotic manipulation, especially when dealing with soft, underactuated, and non-anthropomorphic hands. This work presents a Learning from Demonstration approach to extract grasp primitives using a novel reconfigurable soft hand, the Soft ScoopGripper (SSG). Starting from human demonstrations, we derived Gaussian models through which we were able to devise different grasping strategies, exploiting the SSG features. As the grasping strategies are tightly related to the characteristics of the object to be grasped, we tested two different ways of modeling objects in the training dataset and we comparatively evaluated the resulting primitives. Experimental grasping trials on unknown test objects confirmed the effectiveness of the learned primitives and showed how assuming different levels of knowledge about the object representation in the training phase influences the grasp success.