Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction

This paper introduces a novel neural network-based reinforcement learning approach for robot gaze control. Our approach enables a robot to learn and to adapt its gaze control strategy for human-robot interaction neither with the use of external sensors nor with human supervision. The robot learns to focus its attention onto groups of people from its own audio-visual experiences, independently of the number of people, of their positions and of their physical appearances. In particular, we use a recurrent neural network architecture in combination with Q-learning to find an optimal action-selection policy; we pre-train the network using a simulated environment that mimics realistic scenarios that involve speaking/silent participants, thus avoiding the need of tedious sessions of a robot interacting with people. Our experimental evaluation suggests that the proposed method is robust against parameter estimation, i.e. the parameter values yielded by the method do not have a decisive impact on the performance. The best results are obtained when both audio and visual information is jointly used. Experiments with the Nao robot indicate that our framework is a step forward towards the autonomous learning of socially acceptable gaze behavior.Comment: Paper submitted to Pattern Recognition Letter

Teaching robots parametrized executable plans through spoken interaction

While operating in domestic environments, robots will necessarily face difficulties not envisioned by their developers at programming time. Moreover, the tasks to be performed by a robot will often have to be specialized and/or adapted to the needs of specific users and specific environments. Hence, learning how to operate by interacting with the user seems a key enabling feature to support the introduction of robots in everyday environments. In this paper we contribute a novel approach for learning, through the interaction with the user, task descriptions that are defined as a combination of primitive actions. The proposed approach makes a significant step forward by making task descriptions parametric with respect to domain specific semantic categories. Moreover, by mapping the task representation into a task representation language, we are able to express complex execution paradigms and to revise the learned tasks in a high-level fashion. The approach is evaluated in multiple practical applications with a service robot

On the design, development and experimentation of the ASTRO assistive robot integrated in smart environments

This paper presents the full experience of designing, developing and testing ASTROMOBILE, a system composed of an enhanced robotic platform integrated in an Ambient Intelligent (AmI) infrastructure that was conceived to provide favourable independent living, improved quality of life and efficiency of care for senior citizens. The design and implementation of ASTRO robot was sustained by a multidisciplinary team in which technology developers, designers and end-user representatives collaborated using a user-centred design approach. The key point of this work is to demonstrate the general feasibility and scientific/technical effectiveness of a mobile robotic platform integrated in a smart environment and conceived to provide useful services to humans and in particular to elderly people in domestic environments. The main aspects faced in this paper are related to the design of the ASTRO’s appearance and functionalities by means of a substantial analysis of users’ requirements, the improvement of the ASTRO’s behaviour by means of a smart sensor network able to share information with the robot (Ubiquitous Robotics) and the development of advanced human robot interfaces based on natural language