This paper presents the TIAGo Head, a new tabletop social robot from PAL Robotics, focusing on its capabilities as an HRI platform. We detail the robots’ hardware, highlighting its sensors/actuators and on-board computing; and its software architecture, including social perception, expressive face, a knowledge base, and integration with large language models (LLMs) for natural conversations. We also describe a use-case in a receptionist scenario where TIAGo Head dynamically interacts with travelers by displaying news and conversing.

With the world population ageing and with the number of healthcare users needing assistance and support increasing, healthcare is becoming more costly and, as such, the need to optimise and support independent living for older people is of paramount importance. This paper reviews the user requirements and challenges that are relevant to older adults who prefer to age healthily at home, and how socially assistive robots (SAR) can be used to help them. The main focus is placed on the social robotic application developed for the H2020 SHAPES project to promote Smart Living Environment for healthy ageing. The solution is based on the newest PAL Robotics’ robot ARI, a high-performance social robot and companion designed for a wide range of multi-modal expressive gestures, gaze and personalised behaviour, which is integrated via several Digital Solutions developed within the SHAPES project to improve human-robot interaction and user acceptability for independent living support tasks. The validation process will take place over the coming months at Clinica Humana (Mallorca, Spain), which is a private clinic that provides hospital care to retirement homes, communities and home-bound patients. A description of the scenario definition is presented in the paper, together with the validation plan that will be executed during the pilot assessments and the measures that will be taken to improve user engagement.

With the world population aging and the number of healthcare users with multiple chronic diseases increasing, healthcare is becoming more costly, and as such, the need to optimise both hospital and in-home care is of paramount importance. This paper reviews the challenges that the older people, people with mobility constraints, hospital patients and isolated healthcare users face, and how socially assistive robots can be used to help them. Related promising areas and limitations are highlighted. The main focus is placed on the newest PAL Robotics' robot: ARI, a high-performance social robot and companion designed for a wide range of multi-modal expressive gestures, gaze and personalised behaviour, with great potential to become part of the healthcare community by applying powerful AI algorithms. ARI can be used to help administer first-care attention, providing emotional support to people who live in isolation, including the elderly population or healthcare users who are confined because of infectious diseases such as Covid-19. The ARI robot technical features and potential applications are introduced in this paper.

Consider a differential-drive mobile robot equipped with an on-board exteroceptive sensor that can estimate its own motion, e.g., a range-finder. Calibration of this robot involves estimating six parameters: three for the odometry (radii and distance between the wheels) and three for the pose of the sensor with respect to the robot. After analyzing the observability of this problem, this paper describes a method for calibrating all parameters at the same time, without the need for external sensors or devices, using only the measurement of the wheel velocities and the data from the exteroceptive sensor. The method does not require the robot to move along particular trajectories. Simultaneous calibration is formulated as a maximum-likelihood problem and the solution is found in a closed form. Experimental results show that the accuracy of the proposed calibration method is very close to the attainable limit given by the Cramér-Rao bound.

For a differential-drive mobile robot equipped with an on-board range sensor, there are six parameters to calibrate: three for the odometry (radii and distance between the wheels), and three for the pose of the sensor with respect to the robot frame. This paper describes a method for calibrating all six parameters at the same time, without the need for external sensors or devices. Moreover, it is not necessary to drive the robot along particular trajectories. The available data are the measures of the angular velocities of the wheels and the range sensor readings. The maximum-likelihood calibration solution is found in a closed form.