Using robots as therapeutic or educational tools for children with autism requires robots to be able to adapt their behavior specifically for each child with whom they interact. In particular, some children may like to be looked into the eyes by the robot while some may not. Some may like a robot with an extroverted behavior while others may prefer a more introverted behavior. Here we present an algorithm to adapt the robot's expressivity parameters of action (mutual gaze duration, hand movement expressivity) in an online manner during the interaction. The reward signal used for learning is based on an estimation of the child's mutual engagement with the robot, measured through non-verbal cues such as the child's gaze and distance from the robot. We first present a pilot joint attention task where children with autism interact with a robot whose level of expressivity is predetermined to progressively increase, and show results suggesting the need for online adaptation of expressivity. We then present the proposed learning algorithm and some promising simulations in the same task. Altogether, these results suggest a way to enable robot learning based on non-verbal cues and to cope with the high degree of nonstationarities that can occur during interaction with children.

Mobility disabilities are prevalent in our ageing society and impede activities important for the independent living of elderly people and their quality of life. The goal of this work is to support human mobility and thus enforce fitness and vitality by developing intelligent robotic platforms designed to provide usercentred and natural support for ambulating in indoor environments. We envision the design of cognitive mobile robotic systems that can monitor and understand specific forms of human activity, in order to deduce what the human needs are, in terms of mobility. The goal is to provide user and context adaptive active support and ambulation assistance to elderly users, and generally to individuals with specific forms of moderate to mild walking impairment.To achieve such targets, a reliable multimodal action recognition system needs to be developed, that can monitor, analyse and predict the user actions with a high level of accuracy and detail. Different modalities need to be combined into an integrated action recognition system. This paper reports current advances regarding the development and implementation of the first walking assistance robot prototype, which consists of a sensorized and actuated rollator platform. The main thrust of our approach is based on the enhancement of computer vision techniques with modalities that are broadly used in robotics, such as range images and haptic data, as well as on the integration of machine learning and pattern recognition approaches regarding specific verbal and non-verbal (gestural) commands in the envisaged (physical and non-physical) human-robot interaction context.

In this work, we present a novel framework for on-line human gait stability prediction of the elderly users of an intelligent robotic rollator using Long Short Term Memory (LSTM) networks, fusing multimodal RGB-D and Laser Range Finder (LRF) data from non-wearable sensors. A Deep Learning (DL) based approach is used for the upper body pose estimation. The detected pose is used for estimating the body Center of Mass (CoM) using Unscented Kalman Filter (UKF). An Augmented Gait State Estimation framework exploits the LRF data to estimate the legs' positions and the respective gait phase. These estimates are the inputs of an encoder-decoder sequence to sequence model which predicts the gait stability state as Safe or Fall Risk walking. It is validated with data from real patients, by exploring different network architectures, hyperparameter settings and by comparing the proposed method with other baselines. The presented LSTMbased human gait stability predictor is shown to provide robust predictions of the human stability state, and thus has the potential to be integrated into a general user-adaptive control architecture as a fall-risk alarm.

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