Fall risk assessments provide a useful tool to prevent morbidity and mortality provoked by falls. Nowadays, these assessments are usually performed manually by the medical staff. This approach has three main drawbacks: (i) it is time consuming, so it is only performed a few times per volunteer during their rehabilitation process; (ii) it requires supervision by medical staff, so assessment at home or preferred environments is not feasible; and (iii) fall risk is evaluated in a global way, so imminent fall risk is not available for decision making in assistive navigation. In this paper we propose an imminent fall risk estimator for rollator's users that can be automatically obtained on the fly. Its main advantages are: (i) it can be used in everyday conditions in any environment; (ii) it does not require assistance of medical staff; and (iii) it is suitable for a variety of users with minimal configuration changes. We have validated our estimator with a set of volunteers (n=10) presenting different physical and cognitive disabilities. Although the number of volunteer is limited, results show that our estimator is coherent to two traditional, well accepted assessments: the Tinetti Mobility Test and the walking speed.

Multi-robot systems (MRS) are a very active and important research topic nowadays. One of the main problems of these systems is the large number of variables to take into account. Due to this, robot behaviors are sometimes learnt instead of calculated via analytical expressions. A typical learning mechanism, specially for biomimetic robots, is Learning from demonstration (LfD). This paper proposes a LfD approach for implicit coordinated navigation using combination of Case-Based Reasoning (CBR) behaviors. During a training stage, CBR is used to learn simple behaviors that associate positions of other robots and/or objects to motion commands for each robot. Thus, human operators only need to concentrate on achieving their robot's goal as efficiently as possible in the operating conditions. Then, in running stage, each robot will achieve a different coordinate navigation strategy depending on the triggered behaviors. This system has been successfully tested with three Aibo-ERS7 robots in a RobCup-like environment.

This work presents a new approach to proactive collaborative wheelchair control. The system is based on estimating how much help the person needs at each situation and providing just the correct amount. This is achieved by combining robot and human control commands in a reactive fashion after weighting them by their respective local efficiency. Thus, the better the person drives, the more control he/she is awarded with. In order to predict how much help users may need in advance rather than waiting for them to decrease in efficiency, their skills to deal with each situation are estimated with respect to a baseline driver profile to increase assistance when needed. Situations are characterized at reactive level to keep a reduced set. This profile has been extracted from real traces of more than 70 inpatients presenting different physical and cognitive skills via clustering. The approach has been successfully tested at Fondazione Santa Lucia (FSL) in Rome.

Localization in multi-robot systems is a key problem in multi agent systems. In many cases, specially involving legged robots, like the Robocup soccer competition, it relies on predefined landmarks at known locations. However, when several objects are in motion, vision occlusions due to agents in the field of view make this kind of localization unfeasible for mild time periods. This also happens if landmarks are not within the field of view. This paper presents a technique to let a robot estimate its position with respect to objects or robots by sharing whatever visual data it has with its teammates. Shared data is used to estimate the relative positions of robots watching the same object via stereoscopy. The rest of the robots can be localized via triangulation and acquire information on the position of hidden or unknown objects that other members of the team can see. The system has been tested with two Aibo ERS 7 robots from Sony and an Aiball.

This work presents a new approach to shared control to assist wheelchair driving. Rather than swapping control from human to robot either by request or on a need basis, the system estimates how much help is needed in a reactive fashion and continuously produces an emergent motory command in combination with human input. To provide time stability and integration, instant commands are modulated by a factor depending on human efficiency in a shifting time window. Thus, the better the person drives, the more control he/she is awarded with. The approach has been tested at Fondazione Santa Lucia (FSL) in Rome with volunteers presenting different disabilities. All volunteers managed to finish a mildly complicated trajectory with door crossing and major turns and the proposed system increased efficiency in all cases.