Musculoskeletal disorders (MSD) are the most common cause of work-related injuries and lost production involving approximately 1.7 billion people worldwide and mainly affect low back (more than 50%) and upper limbs (more than 40%). It has a profound effect on both the workers affected and the company. This paper provides an ergonomic assessment of different work activities in a horse saddle-making company, involving 5 workers. This aim guides the design of a wrist exoskeleton to reduce the risk of musculoskeletal diseases wherever it is impossible to automate the production process. This evaluation is done either through subjective and objective measurement, respectively using questionnaires and by measurement of muscle activation with sEMG sensors.

Gaze is a crucial social cue in any interacting scenario and drives many mechanisms of social cognition (joint and shared attention, predicting human intention, coordination tasks). Gaze direction is an indication of social and emotional functions affecting the way the emotions are perceived. Evidence shows that embodied humanoid robots endowing social abilities can be seen as sophisticated stimuli to unravel many mechanisms of human social cognition while increasing engagement and ecological validity. In this context, building a robotic perception system to automatically estimate the human gaze only relying on robot's sensors is still demanding. Main goal of the paper is to propose a learning robotic architecture estimating the human gaze direction in table-top scenarios without any external hardware. Table-top tasks are largely used in many studies in experimental psychology because they are suitable to implement numerous scenarios allowing agents to collaborate while maintaining a face-to-face interaction. Such an architecture can provide a valuable support in studies where external hardware might represent an obstacle to spontaneous human behaviour, especially in environments less controlled than the laboratory (e.g., in clinical settings). A novel dataset was also collected with the humanoid robot iCub, including images annotated from 24 participants in different gaze conditions.

The fusion of Large Language Models (LLMs) and robotic systems has led to a transformative paradigm in the robotic field, offering unparalleled capabilities not only in the communication domain but also in skills like multimodal input handling, high-level reasoning, and plan generation. The grounding of LLMs knowledge into the empirical world has been considered a crucial pathway to exploit the efficiency of LLMs in robotics. Nevertheless, connecting LLMs' representations to the external world with multimodal approaches or with robots' bodies is not enough to let them understand the meaning of the language they are manipulating. Taking inspiration from humans, this work draws attention to three necessary elements for an agent to grasp and experience the world. The roadmap for LLMs grounding is envisaged in an active bodily system as the reference point for experiencing the environment, a temporally structured experience for a coherent, self-related interaction with the external world, and social skills to acquire a common-grounded shared experience.

We study Nyström type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are considered. In particular, we prove that these approaches can achieve optimal learning bounds, provided the subsampling level is suitably chosen. These results suggest a simple incremental variant of Nyström Kernel Regularized Least Squares, where the subsampling level implements a form of computational regularization, in the sense that it controls at the same time regularization and computations. Extensive experimental analysis shows that the considered approach achieves state of the art performances on benchmark large scale datasets.

A humanoid robot can relay video and touch sensations to a person wearing haptic feedback gloves and a virtual reality (VR) headset hundreds of kilometres away, offering a way for people to attend events without travelling. The iCub 3 robot is a 52-kilogram, 125- centimetre-tall robot with 54 points of articulation across its aluminium alloy and plastic body. Its head contains two cameras where a human's eyes would be, and an internet-connected computer where the brain would go. Along with the cameras, sensors covering its body send data to the robot's "brain". These sensations are then replicated on a suit and VR headset worn by a remote human operator.

Researchers at Istituto Italiano di Tecnologia (IIT-Italian Institute of Technology) in Genova has realized a new soft robot inspired by the biology of earthworms,which is able to crawl thanks to soft actuators that elongate or squeeze, when air passes through them or is drawn out. The prototype has been described in the international journal Scientific Reports of the Nature Portfolio, and it is the starting point for developing devices for underground exploration, but also search and rescue operations in confined spaces and the exploration of other planets. Nature offers many examples of animals, such as snakes, earthworms, snails, and caterpillars, which use both the flexibility of their bodies and the ability to generate physical travelling waves along the length of their body to move and explore different environments. Some of their movements are also similar to plant roots. Taking inspiration from nature and, at the same time, revealing new biological phenomena while developing new technologies is the main goal of the BioInspired Soft robotics lab coordinated by Barbara Mazzolai, and this earthworm-like robot is the latest invention coming from her group.

There is a dramatic shortage of skilled labor for modern vineyards. The Vinum project is developing a mobile robotic solution to autonomously navigate through vineyards for winter grapevine pruning. This necessitates an autonomous navigation stack for the robot pruning a vineyard. The Vinum project is using the quadruped robot HyQReal. This paper introduces an architecture for a quadruped robot to autonomously move through a vineyard by identifying and approaching grapevines for pruning. The higher level control is a state machine switching between searching for destination positions, autonomously navigating towards those locations, and stopping for the robot to complete a task. The destination points are determined by identifying grapevine trunks using instance segmentation from a Mask Region-Based Convolutional Neural Network (Mask-RCNN). These detections are sent through a filter to avoid redundancy and remove noisy detections. The combination of these features is the basis for the proposed architecture.

Bio: Antonio Bicchi is a scientist interested in robotics and intelligent machines. After graduating in Pisa and receiving a Ph.D. from the University of Bologna, he spent a few years at the MIT AI Lab of Cambridge before becoming Professor in Robotics at the University of Pisa. In 2009 he founded the Soft Robotics Laboratory at the Italian Institute of Technology in Genoa. Since 2013 he is Adjunct Professor at Arizona State University, Tempe, AZ. He has coordinated many international projects, including four grants from the European Research Council (ERC).

Mobile manipulators that combine mobility and manipulability, are increasingly being used for various unstructured application scenarios in the field, e.g. vineyards. Therefore, the coordinated motion of the mobile base and manipulator is an essential feature of the overall performance. In this paper, we explore a whole-body motion controller of a robot which is composed of a 2-DoFs non-holonomic wheeled mobile base with a 7-DoFs manipulator (non-holonomic wheeled mobile manipulator, NWMM) This robotic platform is designed to efficiently undertake complex grapevine pruning tasks. In the control framework, a task priority coordinated motion of the NWMM is guaranteed. Lower-priority tasks are projected into the null space of the top-priority tasks so that higher-priority tasks are completed without interruption from lower-priority tasks. The proposed controller was evaluated in a grapevine spur pruning experiment scenario.

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).