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Compliant and soft hands have gained a lot of attention in the past decade because of their ability to adapt to the shape of the objects, increasing their effectiveness for grasping. However, when it comes to grasping highly flexible objects such as textiles, we face the dual problem: it is the object that will adapt to the shape of the hand or gripper. In this context, the classic grasp analysis or grasping taxonomies are not suitable for describing textile objects grasps. This work proposes a novel definition of textile object grasps that abstracts from the robotic embodiment or hand shape and recovers concepts from the early neuroscience literature on hand prehension skills. This framework enables us to identify what grasps have been used in literature until now to perform robotic cloth manipulation, and allows for a precise definition of all the tasks that have been tackled in terms of manipulation primitives based on regrasps. In addition, we also review what grippers have been used. Our analysis shows how the vast majority of cloth manipulations have relied only on one type of grasp, and at the same time we identify several tasks that need more variety of grasp types to be executed successfully. Our framework is generic, provides a classification of cloth manipulation primitives and can inspire gripper design and benchmark construction for cloth manipulation. ; The research leading to these results receives funding from the European Research Council (ERC) from the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE: CLOTH manIpulation Learning from DEmonstrations) and is also supported by the Spanish State Research Agency through the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656) and the "Ramon y Cajal" Fellowship RYC-2017-22703. ; Peer reviewed

Trabajo presentado en el 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), celebrado en Nueva Delhi (India), del 14 al 18 de octubre de 2019 ; Collecting data in Human-Robot Interaction for training learning agents might be a hard task to accomplish. This is especially true when the target users are older adults with dementia since this usually requires hours of interactions and puts quite a lot of workload on the user. This paper addresses the problem of importing the Personas technique from HRI to create fictional patients¿ profiles. We propose a Persona-Behaviour Simulator tool that provides, with high-level abstraction, user¿s actions during an HRI task, and we apply it to cognitive training exercises for older adults with dementia. It consists of a Persona Definition that characterizes a patient along four dimensions and a Task Engine that provides information regarding the task complexity. We build a simulated environment where the high-level user¿s actions are provided by the simulator and the robot initial policy is learned using a Q-learning algorithm. The results show that the current simulator provides a reasonable initial policy for a defined Persona profile. Moreover, the learned robot assistance has proved to be robust to potential changes in the user¿s behaviour. In this way, we can speed up the fine-tuning of the rough policy during the real interactions to tailor the assistance to the given user. We believe the presented approach can be easily extended to account for other types of HRI tasks; for example, when input data is required to train a learning algorithm, but data collection is very expensive or unfeasible. We advocate that simulation is a convenient tool in these cases. ; This work has been partially funded by the EU project SOCRATES H2020-MSCA-ITN-721619, by the Spanish Ministry of Science and Innovation HuMoUR TIN2017-90086-R, and by the State Research Agency through the Mara de Maeztu Seal of Excellence to IRI (MDM-2016-0656).

Trabajo presentado a la International Conference on Social Robotics (ICSR), celebrada en Kansas (USA) del 1 al 3 de noviembre de 2016. ; The deployment of robots at home must involve robots with pre-defined skills and the capability of personalizing their behavior by non-expert users. A framework to tackle this personalization is presented and applied to an automatic feeding task. The personalization involves the caregiver providing several examples of feeding using Learning-by-Demostration, and a ProMP formalism to compute an overall trajectory and the variance along the path. Experiments show the validity of the approach in generating different feeding motions to adapt to user¿s preferences, automatically extracting the relevant task parameters. The importance of the nature of the demonstrations is also assessed, and two training strategies are compared. ; This work has been supported by the MINECO project RobInstruct TIN2014-58178-R and the ERA-Net CHIST-ERA project I-DRESS PCIN-2015-147. Gerard Canal is also supported by the Ministry of Economy and Knowledge of the Government of Catalonia via a FI-DGR 2016 fellowship. ; Peer Reviewed

Trabajo presentado al IROS: "Machine Learning in Planning and Control of Robot Motion Workshop" (IROS MLPC), celebrado en Chicago, Illinois (US) del 14 al 18 de septiembre. ; Este ítem (excepto textos e imágenes no creados por el autor) está sujeto a una licencia de Creative Commons: Attribution-NonCommercial-NoDerivs 3.0 Spain. ; Task learning in robotics is a time-consuming process, and model-based reinforcement learning algorithms have been proposed to learn with just a small amount of experiences. However, reducing the number of experiences used to learn implies that the algorithm may overlook crucial actions required to get an optimal behavior. For example, a robot may learn simple policies that have a high risk of not reaching the goal because they often fall into dead-ends. We propose a new method that allows the robot to reason about dead-ends and their causes. Analyzing its current model and experiences, the robot will hypothesize the possible causes for the dead-end, and identify the actions that may cause it, marking them as dangerous. Afterwards, whenever a dangerous action is included into a plan which has a high risk of leading to a dead-end, the special action request teacher confirmation will be triggered by the robot to actively confirm with a teacher that the planned risky action should be executed. This method permits learning safer policies with the addition of just a few teacher demonstration requests. Experimental validation of the approach is provided in two different scenarios: a robotic assembly task and a domain from the international planning competition. Our approach gets success ratios very close to 1 in problems where previous approaches had high probabilities of reaching dead-ends. ; This work was supported by EU Project IntellAct FP7-ICT2009-6-269959, by CSIC project MANIPlus 201350E102 and by the Spanish Ministry of Science and Innovation under project PAU+ DPI2011-27510. D. Martínez is also supported by the Spanish Ministry of Education, Culture and Sport via a FPU doctoral grant (FPU12-04173). ; Peer Reviewed

Cloth manipulation has been mostly advancing in perception and modeling methods for cloth state estimation and grasping point detection. In comparison, less attention has been put on end-effector design. Indeed, most implementations use a parallel gripper that can only perform pinch grasps. Instead, a more versatile set of possible grasp types could ease many tasks by providing more support to certain parts of the object, as well as make feasible tasks that become very complex when executed with only pinch grasps. We present a versatile gripper design which, besides the common open-close thumb+finger(s) feature, it has a couple of reconfiguration degrees of freedom that offer, first, a wide base plane to provide support and, second, variable friction surface on the thumb tip. Our gripper can execute a versatile set of grasps that ease some complex tasks such as pick and place folded clothes or fold in the air. In addition, the variable friction mechanism enables a more robust pinch-and-slide manipulation to trace cloth edges. Our evaluation shows the gripper potential to execute a wide variety of cloth manipulation tasks ; This work receives funding from the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE), Spanish State Research Agency through the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656) and the "Ramon y Cajal" Fellowship RYC-2017- 22703 ; Peer reviewed

Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) represent a major challenge for health systems within the aging population. New and better instruments will be crucial to assess the disease severity and progression, as well as to improve its treatment, stimulation, and rehabilitation. With the purpose of detecting, assessing and quantifying cognitive impairments like MCI or AD, several methods are employed by clinical experts. Syndrom Kurztest neuropsychological battery (SKT) is a simple and short test to measure cognitive decline as it assesses memory, attention, and related cognitive functions, taking into account the speed of information processing. In this paper, we present a decision system to embed in a robot that can set up a productive interaction with a patient, and can be employed by the caregiver to motivate and support him while performing cognitive exercises as SKT. We propose two different interaction loops. First, the robot interacts with the caregiver in order to set up the mental and physical impairments of the patient and indicate a goal of the exercise. This is used to determine the desired robot behavior (human-centric or robot-centric, and preferred interaction modalities). Second, the robot interacts with the patient and adapts its actions to engage and assist him to complete the exercise. Two batches of experiments were conducted, and the results indicated that the robot can take profit of the initial interaction with the caregiver to provide a quicker personalization, and also it can adapt to different user responses and provide support and assistance at different levels of interaction. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie SkłodowskaCurie grant agreement No. 721619 by the Spanish Ministry of Science and Innovation under project HuMoUR TIN2017-90086 and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656) ; Peer reviewed

Trabajo presentado en la IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), celebrada en Seattle (Estados Unidos) del 13 al 19 de junio de 2020 ; The problem of predicting human motion given a sequence of past observations is at the core of many applications in robotics and computer vision. Current state-of-the-art formulate this problem as a sequence-to-sequence task, in which a historical of 3D skeletons feeds a Recurrent Neural Network (RNN) that predicts future movements, typically in the order of 1 to 2 seconds. However, one aspect that has been obviated so far, is the fact that human motion is inherently driven by interactions with objects and/or other humans in the environment. In this paper, we explore this scenario using a novel context-aware motion prediction architecture. We use a semantic-graph model where the nodes parameterize the human and objects in the scene and the edges their mutual interactions. These interactions are iteratively learned through a graph attention layer, fed with the past observations, which now include both object and human body motions. Once this semantic graph is learned, we inject it to a standard RNN to predict future movements of the human/s and object/s. We consider two variants of our architecture, either freezing the contextual interactions in the future of updating them. A thorough evaluation in the "Whole-Body Human Motion Database" [29] shows that in both cases, our context-aware networks clearly outperform baselines in which the context information is not considered. ; This work has been partially funded by the Spanish government under projects HuMoUR TIN2017-90086-R, ERA-Net Chistera project IPALM PCI2019-103386 and Mar´ıa de Maeztu Seal of Excellence MDM-2016-0656. We also thank Nvidia for hardware donation.

Probabilistic planners have improved recently to the point that they can solve difficult tasks with complex and expressive models. In contrast, learners cannot tackle yet the expressive models that planners do, which forces complex models to be mostly handcrafted. We propose a new learning approach that can learn relational probabilistic models with both action effects and exogenous effects. The proposed learning approach combines a multivalued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. We also show how to combine this learner with reinforcement learning algorithms to solve complete problems. Finally, experimental validation is provided that shows improvements over previous work in both simulation and a robotic task. The robotic task involves a dynamic scenario with several agents where a manipulator robot has to clear the tableware on a table. We show that the exogenous effects learned by our approach allowed the robot to clear the table in a more efficient way. ; This work has been supported by the MINECO project RobInstruct TIN2014-58178-R and the European Union's Horizon 2020 research and innovation programme under grant agreement H2020-ICT-2016-1-731761 IMAGINE. D. Martínez is also supported by the Spanish Ministry of Education, Culture and Sport via a FPU doctoral grant (FPU12-04173). ; Peer Reviewed

Trabajo presentado en la IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), celebrada en Seattle (Estados Unidos) del 13 al 19 de junio de 2020 ; The rise of deep learning has brought remarkable progress in estimating hand geometry from images where the hands are part of the scene. This paper focuses on a new problem not explored so far, consisting in predicting how a human would grasp one or several objects, given a single RGB image of these objects. This is a problem with enormous potential in e.g. augmented reality, robotics or prosthetic design. In order to predict feasible grasps, we need to understand the semantic content of the image, its geometric structure and all potential interactions with a hand physical model. To this end, we introduce a generative model that jointly reasons in all these levels and 1) regresses the 3D shape and pose of the objects in the scene; 2) estimates the grasp types; and 3) refines the 51-DoF of a 3D hand model that minimize a graspability loss. To train this model we build the YCB-Affordance dataset, that contains more than 133k images of 21 objects in the YCB-Video dataset. We have annotated these images with more than 28M plausible 3D human grasps according to a 33-class taxonomy. A thorough evaluation in synthetic and real images shows that our model can robustly predict realistic grasps, even in cluttered scenes with multiple objects in close contact. ; This work has been partially funded by the Spanish government with the project HuMoUR TIN2017-90086-R, the ERA-Net Chistera project IPALM PCI2019-103386 and Mar´ıa de Maeztu Seal of Excellence MDM-2016-0656.

Trabajo presentado en la IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), celebrada de forma virtual desde Nashville, TN (Estados Unidos), del 20 al 25 de junio de 2021 ; In this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry. In contrast to existing learning-based approaches that require training specific models for each type of garment, SMPLicit can represent in a unified manner different garment topologies (eg from sleeveless tops to hoodies and to open jackets), while controlling other properties like the garment size or tightness/looseness. We show our model to be applicable to a large variety of garments including T-shirts, hoodies, jackets, shorts, pants, skirts, shoes and even hair. The representation flexibility of SMPLicit builds upon an implicit model conditioned with the SMPL human body parameters and a learnable latent space which is semantically interpretable and aligned with the clothing attributes. The proposed model is fully differentiable, allowing for its use into larger end-to-end trainable systems. In the experimental section, we demonstrate SMPLicit can be readily used for fitting 3D scans and for 3D reconstruction in images of dressed people. In both cases we are able to go beyond state of the art, by retrieving complex garment geometries, handling situations with multiple clothing layers and providing a tool for easy outfit editing. To stimulate further research in this direction, we will make our code and model publicly available at http://www.iri.upc.edu/people/ecorona/smplicit/. ; This work is supported in part by an Amazon Research Award and by the Spanish government with the projects Hu- MoUR TIN2017-90086-R and Mar´ıa de Maeztu Seal of Excellence MDM- 2016-0656. Gerard Pons-Moll is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 409792180 (Emmy Noether Programme, project: Real Virtual Humans)

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