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Digital technologies have become part of our everyday lives and are increasingly acting as intermediaries in our workplaces and personal relationships or even substituting them. The Internet of things, social networks, programs that learn by interacting with humans, assistive and companion robots, computer games with a purpose, serious games for social impact, roboadvisors, webs that offer digital immortality… These tools can, in a short time, modify the job market, flip someone's reputation, transform a district, change our relationships —not just at work, but also within our families and close contacts— or extend what a person leaves behind after dying, which now includes a digital footprint. ; This work has been partly supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme through the project CLOTHILDE – CLOTH manIpulation Learning from DEmonstrations (Advanced Grant agreement No 741930), and the Spanish Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). ; Peer reviewed

The influence of the humanities on the study of a technological subject like robotics needs to rapidly grow, for the simple reason that robotics is becoming a part of humanity: assisting, interacting, and enabling people in an increasing number of ways in daily life. The robotics research community is well aware of the need for such a crossover with the humanities and many joint ventures are being undertaken, such as forums on "Robotics meets the Humanities" at main robotics conferences, the launching of research projects, and the publication of special issues in scientific journals. This cross-cutting has even led to a new discipline: Roboethics, a subfield of applied ethics studying both the positive and negative implications of robotics for individuals and society, with a view to inspire the moral design, development and use of so-called intelligent/autonomous robots, and help prevent their misuse against humankind. The discipline involves two main areas: legal regulation and ethical education. Regarding the former, institutions such as the European Parliament, the South Korean Robot Ethics Charter, the IEEE Standards Association, and the British Standards Institution are developing regulations for robot designers, programmers, and users. There are many options to integrate ethics education (or Humanities) in technological university degrees, ranging from including a professional ethics course in the syllabus, to allowing students to take certain credits or a minor in a Humanities Department, to even offering a combined degree, like the Computer Science and Philosophy degree at the University of Oxford. Prestigious associations such as IEEE and ACM include 18 knowledge areas in their Computer Science curricula, one of which is "Social Issues and Professional Practice", so that "students develop an understanding of the relevant social, ethical, legal and professional issues". To this end, some courses in this area recur to science fiction to exemplify conflictive situations, since narrative is a good way to engage students in safe discussion and reasoning about difficult and emotionally charged issues without making it personal. Some experiences along this line will be described. ; Peer Reviewed ; Postprint (author's final draft)

The influence of the humanities on the study of a technological subject like robotics needs to rapidly grow, for the simple reason that robotics is becoming a part of humanity: assisting, interacting, and enabling people in an increasing number of ways in daily life. The robotics research community is well aware of the need for such a crossover with the humanities and many joint ventures are being undertaken, such as forums on "Robotics meets the Humanities" at main robotics conferences, the launching of research projects, and the publication of special issues in scientific journals. This cross-cutting has even led to a new discipline: Roboethics, a subfield of applied ethics studying both the positive and negative implications of robotics for individuals and society, with a view to inspire the moral design, development and use of so-called intelligent/autonomous robots, and help prevent their misuse against humankind. The discipline involves two main areas: legal regulation and ethical education. Regarding the former, institutions such as the European Parliament, the South Korean Robot Ethics Charter, the IEEE Standards Association, and the British Standards Institution are developing regulations for robot designers, programmers, and users. There are many options to integrate ethics education (or Humanities) in technological university degrees, ranging from including a professional ethics course in the syllabus, to allowing students to take certain credits or a minor in a Humanities Department, to even offering a combined degree, like the Computer Science and Philosophy degree at the University of Oxford. Prestigious associations such as IEEE and ACM include 18 knowledge areas in their Computer Science curricula, one of which is "Social Issues and Professional Practice", so that "students develop an understanding of the relevant social, ethical, legal and professional issues". To this end, some courses in this area recur to science fiction to exemplify conflictive situations, since narrative is a good way to engage students in safe discussion Carme Torras and reasoning about difficult and emotionally charged issues without making it personal. Some experiences along this line will be described ; This work has been partly supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme through the project CLOTHILDE - CLOTH manIpulation Learning from DEmonstrations (Advanced Grant agreement No 741930), and the Spanish Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656).

[EN]Assistive robotics is a fast growing field aimed at helping healthcarers in hospitals, rehabilitation centers and nursery homes, as well as empowering people with reduced mobility at home, so that they can autonomously fulfill their daily living activities. The need to function in dynamic human-centered environments poses new research challenges: robotic assistants need to have friendly interfaces, be highly adaptable and customizable, very compliant and intrinsically safe to people, as well as able to handle deformable materials. Besides technical challenges, assistive robotics raises also ethical defies, which have led to the emergence of a new discipline: Roboethics. Several institutions are developing regulations and standards, and many ethics education initiatives include contents on human-robot interaction and human dignity in assistive situations. In this paper, the state of the art in assistive robotics is briefly reviewed, and educational materials from a university course on Ethics in Social Robotics and AI focusing on the assistive context are presented. ; [ES] La robótica asistencial es un campo en rápido crecimiento des-tinado a ayudar a los cuidadores en hospitales, centros de rehabilitación y residencias, así como a capacitar a las personas con movilidad reducida en el hogar, para que puedan realizar de forma autónoma sus activida-des cotidianas. La necesidad de desempeñarse en entornos dinámicos centrados en el ser humano plantea nuevos retos de investigación: los asistentes robóticos deben tener interfaces amigables, ser altamente adaptables y personalizables, muy compatibles y seguros para las perso-nas, así como ser capaces de manejar materiales deformables.Además de los desafíos técnicos, la robótica asistencial plantea también desafíos éticos, que han llevado a la aparición de una nueva disciplina: la roboética. Numerosas instituciones están elaborando reglamentos y normas, y muchas iniciativas de educación en ética incluyen contenidos sobre la interacción humano-robot y la dignidad humana en situaciones de asistencia.En este trabajo se revisa brevemente el estado del arte de la robótica asistencial y se presentan materiales educativos de un curso universitario sobre Ética de la Robótica Social e IA en el contexto asistencial. ; This work is partly supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme through the project CLOTHIL-DE - CLOTH manIpulation Learning from DEmonstrations (Advanced Grant agreement No 741930), and the Spanish Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656)

The version of record is available online at: https://doi.org/10.1007/s10514-021-09968-7 ; Forty years ago the notion of configuration space (C-space) revolutionised robot motion planning for rigid and articulated objects. Despite great progress, handling deformable materials has remained elusive because of their infinite-dimensional shape-state space, and finding low-complexity representations has become a pressing research goal. This work tries to make a tiny step in this direction by proposing a state representation for textiles relying on the C-space of some distinctive points. A stratification of the C-space Conf_n(R^2) for n points in the cloth is derived from that of the flag manifold, and topological techniques to determine adjacencies in manipulation-centred state graphs are developed. Their algorithmic implementation permits obtaining cloth state-space representations of different granularities and tailored to particular purposes. An example of their usage to distinguish between cloth states having different manipulation affordances is provided, and ways in which the proposed state graphs can serve as a common ground to link the perception, planning and manipulation of textiles are suggested. ; This work is supported by the European Research Council (ERC) within the European Union Horizon 2020 Programme under grant agreement ERC-2016-ADG-741930 (CLOTHILDE: CLOTH manIpulation Learning from DEmonstrations) and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). ; Peer Reviewed ; Postprint (published version)

Given a rectangular piece of cloth on a planar surface, we aim to characterise its states based on the robot manipulations they would require. Considering the cloth as a set of n points in R2, we study its configuration space, Confn(R2). We derive a stratification of Conf4(R2) using that of Flag(3), and we present some techniques that can be used to determine the adjacencies of Confn(R2) and some group actions we can define on it. ; This work is supported by the European Research Council (ERC) within the European Union Horizon 2020 Programme under grant agreement ERC–2016–ADG–741930 (CLOTHILDE: CLOTH manIpulation Learning from DEmonstrations) and by the Spanish State Research Agency through the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM–2016–0656). ; Peer reviewed

This paper proposes to enrich robot motion data with trajectory curvature information. To do so, we use an approximate implementation of a topological feature named writhe, which measures the curling of a closed curve around itself, and its analog feature for two closed curves, namely the linking number. Despite these features have been established for closed curves, their definition allows for a discrete calculation that is well-defined for non-closed curves and can thus provide information about how much a robot trajectory is curling around a line in space. Such lines can be predefined by a user, observed by vision or, in our case, inferred as virtual lines in space around which the robot motion is curling. We use these topological features to augment the data of a trajectory encapsulated as a Movement Primitive (MP). We propose a method to determine how many virtual segments best characterize a trajectory and then find such segments. This results in a generative model that permits modulating curvature to generate new samples, while still staying within the dataset distribution and being able to adapt to contextual variables. ; Thiswork has been carried out within the projectCLOTHILDE ("CLOTH manIpulation Learning from DEmonstrations") funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Advanced Grant agreementNo 741930).Research at IRI is also supported by the Spanish State Research Agency through theMaría de Maeztu Seal of Excellence to IRI MDM-2016-0656.

Forty years ago the notion of configuration space (C-space) revolutionised robot motion planning for rigid and articulated objects. Despite great progress, handling deformable materials has remained elusive because of their infinite-dimensional shape-state space. Finding low-complexity representations has become a pressing research goal. This work tries to make a tiny step in this direction by proposing a state representation for textiles relying on the C-space of some distinctive points. A stratification of the configuration space for n points in the cloth is derived from that of the flag manifold, and topological techniques to determine adjacencies in manipulation-centred state graphs are developed. Their algorithmic implementation permits obtaining cloth state–space representations of different granularities and tailored to particular purposes. An example of their usage to distinguish between cloth states having different manipulation affordances is provided. Suggestions on how the proposed state graphs can serve as a common ground to link the perception, planning and manipulation of textiles are also made. ; This work is supported by the European Research Council (ERC) within the European Union Horizon 2020 Programme under grant agreement ERC-2016-ADG-741930 (CLOTHILDE: CLOTH manIpulation Learning from DEmonstrations) and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). We thank Dr Jordi Sanchez Riera for providing us with cloth simulations.

Dynamic movement primitives (DMPs) are widely used as movement parametrization for learning robot trajectories, because of their linearity in the parameters, rescaling robustness, and continuity. However, when learning a movement with DMPs, a very large number of Gaussian approximations needs to be performed. Adding them up for all joints yields too many parameters to be explored when using reinforcement learning (RL), thus requiring a prohibitive number of experiments/simulations to converge to a solution with a (locally or globally) optimal reward. In this paper, we address the process of simultaneously learning a DMP-characterized robot motion and its underlying joint couplings through linear dimensionality reduction (DR), which will provide valuable qualitative information leading to a reduced and intuitive algebraic description of such motion. The results in the experimental section not only show that we can effectively perform DR on DMPs while learning, but we can also obtain better learning curves, as well as additional information about each motion: linear mappings relating joint values and some latent variables. ; This work was partially developed in the context of the Advanced Grant CLOTHILDE ("CLOTH manIpulation Learning from DEmonstrations"), which has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 741930). This work is also partially funded by CSIC projects MANIPlus (201350E102) and TextilRob (201550E028), and Chist-Era Project I-DRESS (PCIN-2015-147). ; Peer reviewed

The version of record is available online at: https://doi.org/10.1007/s10514-021-09976-7 ; This paper proposes to enrich robot motion data with trajectory curvature information. To do so,we use an approximate implementation of a topological feature named writhe, which measures the curling of a closed curve around itself, and its analog feature for two closed curves, namely the linking number. Despite these features have been established for closed curves, their definition allows for a discrete calculation that is well-defined for non-closed curves and can thus provide information about how much a robot trajectory is curling around a line in space. Such lines can be predefined by a user, observed by vision or, in our case, inferred as virtual lines in space around which the robot motion is curling. We use these topological features to augment the data of a trajectory encapsulated as a Movement Primitive (MP). We propose a method to determine how many virtual segments best characterize a trajectory and then find such segments. This results in a generative model that permits modulating curvature to generate new samples, while still staying within the dataset distribution and being able to adapt to contextual variables. ; This work has been carried out within the project CLOTHILDE ("CLOTH manIpulation Learning from DEmonstrations") funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Advanced Grant agreement No 741930). Research at IRI is also supported 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)

The final publication is available at link.springer.com ; Assistive robots need to be able to perform a large number of tasks that imply some type of cloth manipulation. These tasks include domestic chores such as laundry handling or bed-making, among others, as well as dressing assistance to disabled users. Due to the deformable nature of fabrics, this manipulation requires a strong perceptual feedback. Common perceptual skills that enable robots to complete their cloth manipulation tasks are reviewed here, mainly relying on vision, but also resorting to touch and force. The use of such basic skills is then examined in the context of the different cloth manipulation tasks, be them garment-only applications in the line of performing domestic chores, or involving physical contact with a human as in dressing assistance. ; This work was developed in the context of the project CLOTHILDE ("CLOTH manIpulation Learning from DEmonstra-tions"), which has received funding from the European Research Council (ERC) under the European Union's Horizon2020research and innovation programme (Advanced Grant agreement No 741930). This work is supported by the Spanish StateResearch Agency through the Mar ́ıa de Maeztu Seal of Excellence to IRI (MDM-2016-065 ; Peer Reviewed ; Postprint (author's final draft)

Presentado a la 2ª Jornada de Recerca en Automàtica, Visió i Robòtica (AVR/2006) celebrada en Barcelona (España). ; This communication contains excerpts from the PACO-PLUS proposal to the EU, giving a detailed overview of the project, and depicting the contribution from IRI. The PACO-PLUS project aims at the design of a cognitive robot that is able to develop perceptual, behavioural and cognitive categories in a measurable way and communicate and share these with humans and other artificial agents. Objects and Actions are inseparably intertwined; the so-called Object-Action Complexes are the building blocks of cognition. ; This work was supported by projects: 'Perception, action & cognition through learning of object-action complexes.' (4915), 'Grup de recerca consolidat - ROBÒTICA' (8007). ; The PACO-PLUS project is funded by the EU IST Cognitive Systems Program, under project number FP6-2004-IST-4-27657. Juan Andrade is currently on leave at the Computer Vision Center, UAB, under a Juan de la Cierva Posdoctoral Fellow on project TIC 2003-09291. The IRI Robotics Group is partly funded by DURSI Catalonia Government, as Grup Consolidat de Robòtica, 2005SGR-00038. ; Peer Reviewed

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

Movement primitives (MPs) have been widely adopted for representing and learning robotic movements using reinforcement learning policy search. Probabilistic movement primitives (ProMPs) are a kind of MP based on a stochastic representation over sets of trajectories, able to capture the variability allowed while executing a movement. This approach has proved effective in learning a wide range of robotic movements, but it comes with the necessity of dealing with a high-dimensional space of parameters. This may be a critical problem when learning tasks with two robotic manipulators, and this work proposes an approach to reduce the dimension of the parameter space based on the exploitation of symmetry. A symmetrization method for ProMPs is presented and used to represent two movements, employing a single ProMP for the first arm and a symmetry surface that maps that ProMP to the second arm. This symmetric representation is then adopted in reinforcement learning of bimanual tasks (from user-provided demonstrations), using relative entropy policy search algorithm. The symmetry-based approach developed has been tested in an experiment of cloth manipulation, showing a speed increment in learning the task. ; This work was partially developed in the context of the project CLOTHILDE (>CLOTH manIpulation Learning from DEmonstrations>), which has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Advanced Grant agreement No 741930). This work is supported by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656).