Tracking deformable objects with WISARD networks

In this paper, we investigate a new approach based on WISARD Neural Network for the tracking of non-rigid deformable object. The proposed approach allows deploying an on–line training on the texture and shape features of the object, to adapt in real–time to changes, and to partially cope with occlusions. Moreover, the use of parallel classificatory trained on the same set of images allows tracking the movements of the objects. We evaluate our tracking abilities in the scenario of pizza making that represents a very challenging benchmark to test the approach since in this context the shape of the object to track completely changes during the manipulation

A biologically inspired meta-control navigation system for the Psikharpax rat robot

A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics

Psychometric evaluation supported by a social robot: personality factors and technology acceptance

Robotic psychological assessment is a novel field of research that explores social robots as psychometric tools for providing quick and reliable screening exams. In this study, we involved elderly participants to compare the prototype of a robotic cognitive test with a traditional paper-and-pencil psychometric tool. Moreover, we explored the influence of personality factors and technology acceptance on the testing. Results demonstrate the validity of the robotic assessment conducted under professional supervision. Additionally, results show the positive influence of Openness to experience on the interaction with robot's interfaces, and that some factors influencing technology acceptance, such as Anxiety, Trust, and Intention to use, correlate with the performance in the psychometric tests. Technical feasibility and user acceptance of the robotic platform are also discussed

The Role of Personality Factors and Empathy in the Acceptance and Performance of a Social Robot for Psychometric Evaluations

Research and development in socially assistive robotics have produced several novel applications in the care of senior people. However, some are still unexplored such as their use as psychometric tools allowing for a quick and dependable evaluation of human users’ intellectual capacity. To fully exploit the application of a social robot as a psychometric tool, it is necessary to account for the users’ factors that might influence the interaction with a robot and the evaluation of user cognitive performance. To this end, we invited senior participants to use a prototype of a robot-led cognitive test and analyzed the influence of personality traits and user’s empathy on the cognitive performance and technology acceptance. Results show a positive influence of a personality trait, the “openness to experience”, on the human-robot interaction, and that other factors, such as anxiety, trust, and intention to use, are influencing technology acceptance and correlate the evaluation by psychometric tests

Attentional Mechanism for Sensory-motor Coordination in Behavior-based Robotic Systems

This thesis focuses on the problem of efficiently allocating resources for enhancing the performance of an autonomous robotic agent. Such an agent is expected to operate in complex dynamic environments by continuously monitoring its internal states and the external events. These requirements give raise to countless problems that have populated research in the autonomous robotics community in the last two decades. Among these issues, one of the most relevant is to coordinate different low and high-level behaviors, giving them, from time to time, different priority values both for resource allocation and for action selection processes. The main problem in achieving this requirement is that the number and complexity of the stimuli received by each behavior may be quite high and also the effects on the emerging activity may be very hard to foresee. It is clear that it is not possible for the robotic control system to process all the incoming information, especially for real-time applications. Thus, it becomes necessary to build mechanisms able to guide this sensory input selection process and to choose the best action to perform, assuring an efficient use of the robot limited sensorial and cognitive resources. For this purpose, attentional mechanisms, balancing sensors elaboration and actions execution, can be very useful since they play two main roles: they focus the attention on salient regions of the space and they distribute resources and activities in time. As a result of the application of these mechanisms within the robotic control system for sensory-motor coordination, the robot behavior is improved: the robot becomes able to react faster to task-related or safety-critical stimuli and to opportunely split resources among concurrent behaviors. Attentional mechanisms applied to autonomous robotic systems have already been proposed, but mainly for vision-based robotics; conversely, the contribution introduced by the present thesis is the use of an artificial attentional mechanism suitable both for optimizing the use of resources and for execution monitoring and control

Recommender interfaces: The more human-like, the more humans like

Social robots, when used for information providing, are able to affect humans’ trustworthiness and willingness to interact with them. In this work, we conducted an experimental study aimed at observing if the users’ acceptance of recommendations, as well as their engagement in the interaction, is elicited when using a humanoid robot with respect to a common application on a mobile phone. We conducted an experimental study on movie recommendation where the two interfaces provide the same contents, but through different communication channels. In detail, the robot will attend to the participants in a socially contingent fashion, signaled via head and gaze orientation, speech, eye color and gestures related to the genre of the recommended movie, and the app will provide textual and graphical movie presentation. Results show that while the users perceive the interaction with the mobile application more natural, the social robot is able to enhance the users’ satisfaction and provides a good and stable acceptance rate also when facing participants with various degrees of English proficiency