Achieving social acceptance is one of the main goals of Social Robotic Navigation. Despite this topic has received increasing interest in recent years, most of the research has focused on driving the robotic agent along obstacle-free trajectories, planning around estimates of future human motion to respect personal distances and optimize navigation. However, social interactions in everyday life are also dictated by norms that do not strictly depend on movement, such as when standing at the end of a queue rather than cutting it. In this paper, we propose a novel method to recognize common social scenarios and modify a traditional planner's cost function to adapt to them. This solution enables the robot to carry out different social navigation behaviors that would not arise otherwise, maintaining the robustness of traditional navigation. Our approach allows the robot to learn different social norms with a single learned model, rather than having different modules for each task. As a proof of concept, we consider the tasks of queuing and respect interaction spaces of groups of people talking to one another, but the method can be extended to other human activities that do not involve motion.

Service robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and infield validation campaigns hinder the progress of large-scale autonomous platforms. Simulated environments and deep visual perception are spreading as successful tools to speed up the development of robust navigation with low-cost RGB-D cameras. In this context, the contribution of this work is twofold: a synthetic dataset to train deep semantic segmentation networks together with a collection of virtual scenarios for a fast evaluation of navigation algorithms. Moreover, an automatic parametric approach is developed to explore different field geometries and features. The simulation framework and the dataset have been evaluated by training a deep segmentation network on different crops and benchmarking the resulting navigation.

In recent years, population ageing and pandemics have been demonstrated to cause isolation of older adults in their houses, generating the need for a reliable assistive figure. Service robotics recently emerged as high-tech support to the problem, providing a series of aid functionality to satisfy daily indoor assistance. Robotic solutions take care of interactive social aspects [1] or monitoring the health status of the user [2, 3]. Domestic environments are often very demanding for autonomous navigation systems due to the variety of complex and dynamic obstacles they can feature. To this end, the robot platform shall provide extreme flexibility and effective mobility to handle narrow passages thought for humans. Moreover, in order to properly assist the user, the platform should be able to follow them within this environment. Person following [4, 5] is the first step to enable any visual or vocal interaction with the user while monitoring its condition to intervene earlier in the case of anomalous events. Person following systems are often based on naive visual-control strategy, directly coupling the generation of heuristic commands for the robot with the person coordinate in the image [6]. Deep Reinforcement Learning (DRL) agents recently demonstrated significant autonomy and flexibility boost in robotic solutions.

Autonomous navigation algorithms aim at providing mobile robots with efficient planning and control policies to go through cluttered and dynamic environments. Advanced autonomous navigation systems have been explored to improve planners' and controllers' robustness, reliability, and computational efficiency in real-world applications. In the last decade, learning methods have seen a tremendous success among robotics researchers, motivating an increasing collection of innovative works which adopt Deep Reinforcement Learning (DRL) for general autonomous navigation [1], socially aware path planning [2], and agile aerial vehicles autopilot [3]. Besides the most common paradigm of sensorimotor agents or local planners, learning agents can be successfully mixed up in alternative ways with the navigation system. Recent works proposed hybrid solutions to optimize classic planners like the Dynamic Window Approach (DWA) [4]. Moreover, [5, 6] recently showed the effectiveness of the planner's parameters learning approach compared to end-to-end policy learning, resulting in an adaptive optimized planner.

Technology is progressively reshaping the domestic environment as we know it, enhancing home security and the overall ambient quality through smart connected devices. However, demographic shift and pandemics recently demonstrate to cause isolation of elderly people in their houses, generating the need for a reliable assistive figure. Robotic assistants are the new frontier of innovation for domestic welfare. Elderly monitoring is only one of the possible service applications an intelligent robotic platform can handle for collective wellbeing. In this paper, we present Marvin, a novel assistive robot we developed with a modular layer-based architecture, merging a flexible mechanical design with state-of-the-art Artificial Intelligence for perception and vocal control. With respect to previous works on robotic assistants, we propose an omnidirectional platform provided with four mecanum wheels, which enable autonomous navigation in conjunction with efficient obstacle avoidance in cluttered environments. Moreover, we design a controllable positioning device to extend the visual range of sensors and to improve the access to the user interface for telepresence and connectivity. Lightweight deep learning solutions for visual perception, person pose classification and vocal command completely run on the embedded hardware of the robot, avoiding privacy issues arising from private data collection on cloud services.