Learning cloth manipulation with demonstrations

Recent advances in Deep Reinforcement learning and computational capabilities of GPUs have led to variety of research being conducted in the learning side of robotics. The main aim being that of making autonomous robots that are capable of learning how to solve a task on their own with minimal requirement for engineering on the planning, vision, or control side. Efforts have been made to learn the manipulation of rigid objects through the help of human demonstrations, specifically in the tasks such as stacking of multiple blocks on top of each other, inserting a pin into a hole, etc. These Deep RL algorithms successfully learn how to complete a task involving the manipulation of rigid objects, but autonomous manipulation of textile objects such as clothes through Deep RL algorithms is still not being studied in the community. The main objectives of this work involve, 1) implementing the state of the art Deep RL algorithms for rigid object manipulation and getting a deep understanding of the working of these various algorithms, 2) Creating an open-source simulation environment for simulating textile objects such as clothes, 3) Designing Deep RL algorithms for learning autonomous manipulation of textile objects through demonstrations.Peer ReviewedPreprin

Learning robot policies using a high-level abstraction persona-behaviour simulator

2019 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 worksCollecting 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.Peer ReviewedPostprint (author's final draft

Zoom control to compensate camera translation within a robot egomotion estimation approach

The final publication is available at link.springer.comZoom control has not received the attention one would expect in view of how it enriches the competences of a vision system. The possibility of changing the size of object projections not only permits analysing objects at a higher resolution, but it also may improve tracking and, therefore, subsequent 3D motion estimation and reconstruction results. Of further interest to us, zoom control enables much larger camera motions, while fixating on the same target, than it would be possible with fixed focal length cameras.This work is partially funded by the EU PACO-PLUS project FP6-2004-IST- 4-27657. The authors thank Gabriel Pi for their contribution in preparing the experiments.Peer ReviewedPostprint (author's final draft

Monocular object pose computation with the foveal-peripheral camera of the humanoid robot Armar-III

Active contour modelling is useful to fit non-textured objects, and algorithms have been developed to recover the motion of an object and its uncertainty. Here we show that these algorithms can be used also with point features matched in textured objects, and that active contours and point matches complement in a natural way. In the same manner we also show that depth-from-zoom algorithms, developed for zooming cameras, can be exploited also in the foveal-peripheral eye configuration present in the Armar-III humanoid robot.Peer Reviewe

Zoom control to compensate camera translation within a robot egomotion estimation approach

We previously proposed a method to estimate robot egomotion from the deformation of a contour in the images acquired by a robot-mounted camera [2, 1]. The fact that the contour should always be viewed under weak-perspective conditions limits the applicability of the method. In this paper, we overcome this limitation by controlling the zoom so as to compensate for robot translation along the optic axis. Our control entails minimizing an error signal derived directly from image measurements, without requiring any 3D information. Moreover, contrarily to other 2D control approaches, no point correspondences are needed, since a parametric measure of contour deformation suffices. As a further advantage, the error signal is obtained as a byproduct of egomotion estimation and, therefore, it does not introduce any burden in the computation. Experimental results validate this zooming extension to the method. Moreover, robot translations are correctly computed, including those along the optic axis.Peer Reviewe

Adapting robot task planning to user preferences: an assistive shoe dressing example

The final publication is available at link.springer.comHealthcare robots will be the next big advance in humans’ domestic welfare, with robots able to assist elderly people and users with disabilities. However, each user has his/her own preferences, needs and abilities. Therefore, robotic assistants will need to adapt to them, behaving accordingly. Towards this goal, we propose a method to perform behavior adaptation to the user preferences, using symbolic task planning. A user model is built from the user’s answers to simple questions with a fuzzy inference system, and it is then integrated into the planning domain. We describe an adaptation method based on both the user satisfaction and the execution outcome, depending on which penalizations are applied to the planner’s rules. We demonstrate the application of the adaptation method in a simple shoe-fitting scenario, with experiments performed in a simulated user environment. The results show quick behavior adaptation, even when the user behavior changes, as well as robustness to wrong inference of the initial user model. Finally, some insights in a non-simulated world shoe-fitting setup are also provided.Peer ReviewedPostprint (author's final draft

Estimació del moviment de robots mitjançant contorns actius

Aquesta tesi versa sobre l'estimació del moviment d'un robot mòbil a partir dels canvis en les imatges captades per una càmera muntada sobre el robot. El moviment es dedueix amb un algorisme prèviament proposat en el marc de la navegació qualitativa. Per tal d'emprar aquest algorisme en casos reals s'ha fet un estudi de la seva precisió. Per augmentar-ne l'aplicabilitat, s'ha adaptat l'algorisme al cas d'una càmera amb moviments d'orientació i de zoom.Quan els efectes perspectius no són importants, dues vistes d'una escena captades pel robot es poden relacionar amb una transformació afí (o afinitat), que normalment es calcula a partir de correspondències de punts. En aquesta tesi es vol seguir un enfoc alternatiu, i alhora complementari, fent servir la silueta d'un objecte modelada mitjançant un contorn actiu. El marc es el següent: a mesura que el robot es va movent, la projecció de l'objecte a la imatge va canviant i el contorn actiu es deforma convenientment per adaptar-s'hi; de les deformacions d'aquest contorn, expressades en espai de forma, se'n pot extreure el moviment del robot fins a un factor d'escala. Els contorns actius es caracteritzen per la rapidesa en la seva extracció i la seva robustesa a oclusions parcials. A més, un contorn és fàcil de trobar fins i tot en escenes poc texturades, on sovint és difícil trobar punts característics i la seva correspondència.La primera part d'aquest treball té l'objectiu de caracteritzar la precisió i la incertesa en l'estimació del moviment. Per avaluar la precisió, primer es duen a terme un parell d'experiències pràctiques, que mostren la potencialitat de l'algorisme en entorns reals i amb diferents robots. Estudiant la geometria epipolar que relaciona dues vistes d'un objecte planar es demostra que la direcció epipolar afí es pot recuperar en el cas que el moviment de la càmera estigui lliure de ciclorotació. Amb una bateria d'experiments, tant en simulació com reals, es fa servir la direcció epipolar per caracteritzar la precisió global de l'afinitat en diferents situacions, com ara, davant de diferents formes dels contorns, condicions de visualització extremes i soroll al sistema.Pel que fa a la incertesa, gràcies a que la implementació es basa en el filtre de Kalman, per a cada estimació del moviment també es té una estimació de la incertesa associada, però expressada en espai de forma. Per tal propagar la incertesa de l'espai de forma a l'espai de moviment 3D s'han seguit dos camins diferents: un analític i l'altre estadístic. Aquest estudi ha permès determinar quins graus de llibertat es recuperen amb més precisió, i quines correlacions existeixen entre les diferents components. Finalment, s'ha desenvolupat un algorisme que permet propagar la incertesa del moviment en temps de vídeo. Una de les limitacions més importants d'aquesta metodologia és que cal que la projecció de l'objecte estigui dins de la imatge i en condicions de visualització de perspectiva dèbil durant tota la seqüència. En la segona part d'aquest treball, s'estudia el seguiment de contorns actius en el marc de la visió activa per tal de superar aquesta limitació. És una relació natural, atès que el seguiment de contorns actius es pot veure com una tècnica per fixar el focus d'atenció. En primer lloc, s'han estudiat les propietats de les càmeres amb zoom i s'ha proposat un nou algorisme per determinar la profunditat de la càmera respecte a un objecte qualsevol. L'algorisme inclou un senzill calibratge geomètric que no implica cap coneixement sobre els paràmetres interns de la càmera. Finalment, per tal d'orientar la càmera adequadament, compensant en la mesura del possible els moviments del robot, s'ha desenvolupat un algorisme per al control dels mecanismes de zoom, capcineig i guinyada, i s'ha adaptat l'algorisme d'estimació del moviment incorporant-hi els girs coneguts del capcineig i la guinyada.This thesis deals with the motion estimation of a mobile robot from changes in the images acquired by a camera mounted on the robot itself. The motion is deduced with an algorithm previously proposed in the framework of qualitative navigation. In order to employ this algorithm in real situations, a study of its accuracy has been performed. Moreover, relationships with the active vision paradigm have been analyzed, leading to an increase in its applicability.When perspective effects are not significant, two views of a scene are related by an affine transformation (or affinity), that it is usually computed from point correspondences. In this thesis we explore an alternative and at the same time complementary approach, using the contour of an object modeled by means of an active contour. The framework is the following: when the robot moves, the projection of the object in the image changes and the active contour adapts conveniently to it; from the deformation of this contour, expressed in shape space, the robot egomotion can be extracted up to a scale factor. Active contours are characterized by the speed of their extraction and their robustness to partial occlusions. Moreover, a contour is easy to find even in poorly textured scenes, where often it is difficult to find point features and their correspondences.The goal of the first part of this work is to characterize the accuracy and the uncertainty in the motion estimation. Some practical experiences are carried out to evaluate the accuracy, showing the potentiality of the algorithm in real environments and with different robots. We have studied also the epipolar geometry relating two views of a planar object. We prove that the affine epipolar direction between two images can be recovered from a shape vector when the camera motion is free of cyclorotation. With a battery of simulated as well as real experiments, the epipolar direction allows us to analyze the global accuracy of the affinity in a variety of situations: different contour shapes, extreme visualization conditions and presence of noise.Regarding uncertainty, since the implementation is based on a Kalman filter, for each motion estimate we have also its covariance matrix expressed in shape space. In order to propagate the uncertainty from shape space to 3D motion space, two different approaches have been followed: an analytical and a statistical one. This study has allowed us to determine which degrees of freedom are recovered with more accuracy, and what correlations exist between the different motion components. Finally, an algorithm to propagate the motion uncertainty at video rate has been proposed.One of the most important limitations of this methodology is that the object must project onto the image under weak-perspective visualization conditions all along the sequence. In the second part of this work, active contour tracking is studied within the framework of active vision to overcome this limitation. Both relate naturally, as active contour tracking can be seen as a focus-of-attention strategy.First, the properties of zooming cameras are studied and a new algorithm is proposed to estimate the depth of the camera with respect to an object. The algorithm includes a simple geometric calibration that does not require any knowledge about the camera internal parameters.Finally, in order to orientate the camera so as to suitably compensate for robot motion when possible, a new algorithm has been proposed for the control of zoom, pan and tilt mechanisms, and the motion estimation algorithm has been updated conveniently to incorporate the active camera state information

On inferring intentions in shared tasks for industrial collaborative robots

Inferring human operators' actions in shared collaborative tasks, plays a crucial role in enhancing the cognitive capabilities of industrial robots. In all these incipient collaborative robotic applications, humans and robots not only should share space but also forces and the execution of a task. In this article, we present a robotic system which is able to identify different human's intentions and to adapt its behavior consequently, only by means of force data. In order to accomplish this aim, three major contributions are presented: (a) force-based operator's intent recognition, (b) force-based dataset of physical human-robot interaction and (c) validation of the whole system in a scenario inspired by a realistic industrial application. This work is an important step towards a more natural and user-friendly manner of physical human-robot interaction in scenarios where humans and robots collaborate in the accomplishment of a task.Peer ReviewedPostprint (published version

Planning trajectories for the Esautomatix workcell

The iri_twin_staubli_plan_execute_trajectory package allows to plan trajectories in the esautomatix cell for both, simulation in Gazebo and execution in the real robots. The esautomatix_write_trajectory package allows you to record the trajectories of the two Staubli arms at the same time or for a single arm, to be able to reproduce them afterwards.Preprin

Towards safety in physically assistive robots: eating assistance

Safety is one of the base elements to build trust in robots. This paper studies remedies to unavoidable collisions using robotics assistive feeding as an example task. Firstly, we propose an attention mechanism so the user can control the robot using gestures and thus prevent collisions. Secondly, when unwanted contacts are unavoidable we compare two safety strategies: active safety, using a force sensor to monitor maximum allowed forces; and passive safety using compliant controllers. Experimental evaluation shows that the gesture mechanism is effective to control the robot. Also, the impact forces obtained with both methods are similar and thus can be used independently. Additionally, users experimenting on purpose impacts declared that the impact was not harmful.Peer ReviewedPostprint (author's final draft