Active learning based on computer vision and human-robot interaction for the user profiling and behavior personalization of an autonomous social robot

Social robots coexist with humans in situations where they have to exhibit proper communication skills. Since users may have different features and communicative procedures, personalizing human-robot interactions is essential for the success of these interactions. This manuscript presents Active Learning based on computer vision and human-robot interaction for user recognition and profiling to personalize robot behavior. The system identifies people using Intel-face-detection-retail-004 and FaceNet for face recognition and obtains users" information through interaction. The system aims to improve human-robot interaction by (i) using online learning to allow the robot to identify the users and (ii) retrieving users' information to fill out their profiles and adapt the robot's behavior. Since user information is necessary for adapting the robot for each interaction, we hypothesized that users would consider creating their profile by interacting with the robot more entertaining and easier than taking a survey. We validated our hypothesis with three scenarios: the participants completed their profiles using an online survey, by interacting with a dull robot, or with a cheerful robot. The results show that participants gave the cheerful robot a higher usability score (82.14/100 points), and they were more entertained while creating their profiles with the cheerful robot than in the other scenarios. Statistically significant differences in the usability were found between the scenarios using the robot and the scenario that involved the online survey. Finally, we show two scenarios in which the robot interacts with a known user and an unknown user to demonstrate how it adapts to the situation.The research leading to these results has received funding from the projects: Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), RTI2018-096338-B-I00, funded by the Spain Ministry of Science, Innovation and Universities; Robots sociales para mitigar la soledad y el aislamiento en mayores (SOROLI), PID2021-123941OA-I00, funded by Agencia Estatal de Investigación (AEI), Spain Ministry of Science and Innovation. This publication is part of the R&D&I project PLEC2021-007819 funded by MCIN/AEI/10.13039/5011000-11033 and by the European Union NextGenerationEU/PRTR

Multi-Robot Symbolic Task and Motion Planning Leveraging Human Trust Models: Theory and Applications

Multi-robot systems (MRS) can accomplish more complex tasks with two or more robots and have produced a broad set of applications. The presence of a human operator in an MRS can guarantee the safety of the task performing, but the human operators can be subject to heavier stress and cognitive workload in collaboration with the MRS than the single robot. It is significant for the MRS to have the provable correct task and motion planning solution for a complex task. That can reduce the human workload during supervising the task and improve the reliability of human-MRS collaboration. This dissertation relies on formal verification to provide the provable-correct solution for the robotic system. One of the challenges in task and motion planning under temporal logic task specifications is developing computationally efficient MRS frameworks. The dissertation first presents an automaton-based task and motion planning framework for MRS to satisfy finite words of linear temporal logic (LTL) task specifications in parallel and concurrently. Furthermore, the dissertation develops a computational trust model to improve the human-MRS collaboration for a motion task. Notably, the current works commonly underemphasize the environmental attributes when investigating the impacting factors of human trust in robots. Our computational trust model builds a linear state-space (LSS) equation to capture the influence of environment attributes on human trust in an MRS. A Bayesian optimization based experimental design (BOED) is proposed to sequentially learn the human-MRS trust model parameters in a data-efficient way. Finally, the dissertation shapes a reward function for the human-MRS collaborated complex task by referring to the above LTL task specification and computational trust model. A Bayesian active reinforcement learning (RL) algorithm is used to concurrently learn the shaped reward function and explore the most trustworthy task and motion planning solution

Teacher-Aware Active Robot Learning

| openaire: EC/H2020/637991/EU//COMPUTEDThis paper investigates Active Robot Learning strategies that take into account the effort of the user in an interactive learning scenario. Most research claims that Active Learning's sample efficiency can reduce training time and therefore the effort of the human teacher. We argue that the performance driven query selection of standard Active Learning can make the job of the human teacher difficult, resulting in a decrease in training quality due to slowdowns or increased error rates. We investigate this issue by proposing a learning strategy that aims to minimize the user's workload by taking into account the flow of the questions. We compare this strategy against a standard Active Learning strategy based on uncertainty sampling and a third strategy being an hybrid of the two. After studying in simulation the validity and the behavior of these approaches, we conducted a user study where 26 subjects interacted with a NAO robot embodying the presented strategies. We reports results from both the robot's performance and the human teacher's perspectives, observing how the hybrid strategy represents a good compromise between learning performance and user's experienced workload. Based on the results, we provide recommendations on the development of Active Robot Learning strategies going beyond robot's performance.Peer reviewe