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.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This paper reviews the state-of-the art in the field of lock-in time-of-flight (ToF) cameras, their advantages, their limitations, the existing calibration methods, and the way they are being used, ...sometimes in combination with other sensors. Even though lock-in ToF cameras provide neither higher resolution nor larger ambiguity-free range compared to other range map estimation systems, advantages such as registered depth and intensity data at a high frame rate, compact design, low weight, and reduced power consumption have motivated their increasing usage in several research areas, such as computer graphics, machine vision, and robotics.
Robots are becoming safe and smart enough to work alongside people not only on manufacturing production lines, but also in spaces such as houses, museums, or hospitals. This can be significantly ...exploited in situations in which a human needs the help of another person to perform a task, because a robot may take the role of the helper. In this sense, a human and the robotic assistant may cooperatively carry out a variety of tasks, therefore requiring the robot to communicate with the person, understand his/her needs, and behave accordingly. To achieve this, we propose a framework for a user to teach a robot collaborative skills from demonstrations. We mainly focus on tasks involving physical contact with the user, in which not only position, but also force sensing and compliance become highly relevant. Specifically, we present an approach that combines probabilistic learning, dynamical systems, and stiffness estimation to encode the robot behavior along the task. Our method allows a robot to learn not only trajectory following skills, but also impedance behaviors. To show the functionality and flexibility of our approach, two different testbeds are used: a transportation task and a collaborative table assembly.
Motivated by the need of a robust and practical inverse kinematics (IK) algorithm for the WAM robot arm, we reviewed the most used closed-loop IK methods for redundant robots, analyzing their main ...points of concern: convergence, numerical error, singularity handling, joint limit avoidance, and the capability of reaching secondary goals. As a result of the experimental comparison, we propose two enhancements. The first is a new filter for the singular values of the Jacobian matrix that guarantees that its conditioning remains stable, while none of the filters found in the literature is successful at doing so. The second is to combine a continuous task priority strategy with selective damping to generate smoother trajectories. Experimentation on the WAM robot arm shows that these two enhancements yield an IK algorithm that improves on the reviewed state-of-the-art ones, in terms of the good compromise it achieves between time step length, Jacobian conditioning, multiple task performance, and computational time, thus constituting a very solid option in practice. This proposal is general and applicable to other redundant robots.
•We propose an algorithm that first, identifies the type of the garment and second, performs a search of the two grasping points that allow a robot to bring the garment to a known pose.•Using Maya, ...we generate a database of depth images from simulated garments. The whole process is automatized by a code we make public.•We combine depth images from real garments with simulated data, to train a Convolutional Neural Network that significantly improves state of the art results in cloth recognition.•To detect the visibility and Cartesian location of the reference points, we use two more Convolutional Neural Networks per garment. The garment manipulation we propose differs from the classical approach based on re-grasping of the lowest hanging parts.
Identification and bi-manual handling of deformable objects, like textiles, is one of the most challenging tasks in the field of industrial and service robotics. Their unpredictable shape and pose makes it very difficult to identify the type of garment and locate the most relevant parts that can be used for grasping. In this paper, we propose an algorithm that first, identifies the type of garment and second, performs a search of the two grasping points that allow a robot to bring the garment to a known pose. We show that using an active search strategy it is possible to grasp a garment directly from predefined grasping points, as opposed to the usual approach based on multiple re-graspings of the lowest hanging parts. Our approach uses a hierarchy of three Convolutional Neural Networks (CNNs) with different levels of specialization, trained both with synthetic and real images. The results obtained in the three steps (recognition, first grasping point, second grasping point) are promising. Experiments with real robots show that most of the errors are due to unsuccessful grasps and not to the localization of the grasping points, thus a more robust grasping strategy is required.
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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.
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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.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This paper proposes an end-to-end learning from demonstration framework for teaching force-based manipulation tasks to robots. The strengths of this work are manyfold. First, we deal with the problem ...of learning through force perceptions exclusively. Second, we propose to exploit haptic feedback both as a means for improving teacher demonstrations and as a human–robot interaction tool, establishing a bidirectional communication channel between the teacher and the robot, in contrast to the works using kinesthetic teaching. Third, we address the well-known
what to imitate?
problem from a different point of view, based on the mutual information between perceptions and actions. Lastly, the teacher’s demonstrations are encoded using a Hidden Markov Model, and the robot execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the probabilistic model, needed when tackling tasks with ambiguous perceptions. Experimental results show that the robot is able to learn and reproduce two different manipulation tasks, with a performance comparable to the teacher’s one.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Along with its potential contributions to the practice of care, social assistive robotics raises significant ethical issues. The growing development of this technoscientific field of intelligent ...robotics has thus triggered a widespread proliferation of ethical attention towards its disruptive potential. However, the current landscape of ethical debate is fragmented and conceptually disordered, endangering ethics’ practical strength for normatively addressing these challenges. This paper presents a critical literature review of the ethical issues of social assistive robotics, which provides a comprehensive and intelligible overview of the current ethical approach to this technoscientific field. On the one hand, ethical issues have been identified, quantitatively analyzed and categorized in three main thematic groups. Namely: Well-being, Care, and Justice. On the other hand –and on the basis of some significant disclosed tendencies of the current approach–, future lines of research and issues regarding the enrichment of the ethical gaze on social assistive robotics have been identified and outlined.
•The current landscape of ethical debate on social assistive robotics is fragmented and conceptually disordered.•This review analyses the ethical issues of social assistive robotics, categorizing them in three thematic groups (Well-being, Care and Justice)•Significant tendencies of the ethical approach to social assistive robotics have been disclosed and future research lines have been outlined.
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In recent years there has been an increasing interest in deploying robotic systems in public environments able to effectively interact with people. To properly work in the wild, such systems should ...be robust and be able to deal with complex and unpredictable events that seldom happen in controlled laboratory conditions. Moreover, having to deal with untrained users adds further complexity to the problem and makes the task of defining effective interactions especially difficult. In this work, a Cognitive System that relies on planning is extended with adaptive capabilities and embedded in a Tiago robot. The result is a system able to help a person to complete a predefined game by offering various degrees of assistance. The robot may decide to change the level of assistance depending on factors such as the state of the game or the user performance at a given time. We conducted two days of experiments during a public fair. We selected random users to interact with the robot and only for one time. We show that, despite the short-term nature of human–robot interactions, the robot can effectively adapt its way of providing help, leading to better user performances as compared to a robot not providing this degree of flexibility.
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