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Trabajo presentado en el 13th Annual ACM/IEEE International Conference on Human Robot Interaction, celebrado en Chicago del 5 al 8 de marzo de 2018 ; The majority of socially assistive robots interact with their users using multiple modalities. Multimodality is an important feature that can enable them to adapt to the user behavior and the environment. In this work, we propose a resource-based modality-selection algorithm that adjusts the use of the robot interaction modalities taking into account the available resources to keep the interaction with the user comfortable and safe. For example, the robot should not enter the board space while the user is occupying it, or speak while the user is speaking. We performed a pilot study in which the robot acted as a caregiver in cognitive training. We compared a system with the proposed algorithm to a baseline system that uses all modalities for all actions unconditionally. Results of the study suggest that a reduced complexity of interaction does not significantly affect the user experience, and may improve task performance. ; This work was supported by the SOCRATES project funded from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 721619. ; Peer reviewed

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ; Learning from Demonstration (LfD) is a paradigm that allows robots to learn complex manipulation tasks that can not be easily scripted, but can be demonstrated by a human teacher. One of the challenges of LfD is to enable robots to acquire skills that can be adapted to different scenarios. In this paper, we propose to achieve this by exploiting the variations in the demonstrations to retrieve an adaptive and robust policy, using Gaussian Process (GP) models. Adaptability is enhanced by incorporating task parameters into the model, which encode different specifications within the same task. With our formulation, these parameters can be either real, integer, or categorical. Furthermore, we propose a GP design that exploits the structure of replications, i.e., repeated demonstrations with identical conditions within data. Our method significantly reduces the computational cost of model fitting in complex tasks, where replications are essential to obtain a robust model. We illustrate our approach through several experiments on a handwritten letter demonstration dataset. ; This work has been partially funded by the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE) and by the Spanish State Research Agency through the Mar ́ıa de Maeztu Seal of Excellence to IRI [MDM-2016-0656]. ; Peer Reviewed ; Postprint (author's final draft)

Recent studies have revealed the key importance of modelling personality in robots to improve interaction quality by empowering them with social-intelligence capabilities. Most research relies on verbal and non-verbal features related to personality traits that are highly context-dependent. Hence, analysing how humans behave in a given context is crucial to evaluate which of those social cues are effective. For this purpose, we designed an assistive memory game, in which participants were asked to play the game obtaining support from an introvert or extroverted helper, whether from a human or robot. In this context, we aim to (i) explore whether selective verbal and non-verbal social cues related to personality can be modelled in a robot, (ii) evaluate the efficiency of a statistical decision-making algorithm employed by the robot to provide adaptive assistance, and (iii) assess the validity of the similarity attraction principle. Specifically, we conducted two user studies. In the human–human study (N=31), we explored the effects of helper's personality on participants' performance and extracted distinctive verbal and non-verbal social cues from the human helper. In the human–robot study (N=24), we modelled the extracted social cues in the robot and evaluated its effectiveness on participants' performance. Our findings showed that participants were able to distinguish between robots' personalities, and not between the level of autonomy of the robot (Wizard-of-Oz vs fully autonomous). Finally, we found that participants achieved better performance with a robot helper that had a similar personality to them, or a human helper that had a different personality. ; This work is supported by the German Research Foundation (DFG), National Natural Science Foundation of China (NSFC), under project Transregio Crossmodal Learning (DFG TRR 169/NSFC 61621136008), and a CAS-DAAD joint fellowship. This project has also been partially funded by the European Union's Horizon 2020 research and innovation programme under the Marie Skł, odowska-Curie grant agreement SOCRATES (no. 721619), by the European Union's Horizon 2020 under grant agreement CLOTHILDE (no. 741930), by the Spanish Ministry of Science and Innovation HuMoUR (TIN2017-90086-R), and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656).

Cloth manipulation is a challenging task that, despite its importance, has received relatively little attention compared to rigid object manipulation. In this paper, we provide three benchmarks for evaluation and comparison of different approaches towards three basic tasks in cloth manipulation: spreading a tablecloth over a table, folding a towel, and dressing. The tasks can be executed on any bimanual robotic platform and the objects involved in the tasks are standardized and easy to acquire. We provide several complexity levels for each task, and describe the quality measures to evaluate task execution. Furthermore, we provide baseline solutions for all the tasks and evaluate them according to the proposed metrics. ; This work receives funding from the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE), Spanish State Research Agency through the BURG project (CHIST-ERA - PCIN2019- 103447) and the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016- 0656), the "Ramon y Cajal" Fellowship RYC-2017-22703, the Knut and Alice Wallenberg Foundation, and the Swedish Research Council. ; Peer reviewed