Can social interaction skills be taught by a social agent? The role of a robotic mediator in autism therapy

Increasingly socially intelligent agents (software or robotic) are used in education, rehabilitation and therapy. This paper discusses the role of interactive, mobile robots as social mediators in the particular domain of autism therapy. This research is part of the project AURORA that studies how mobile robots can be used to teach children with autism basic interaction skills that are important in social interactions among humans. Results from a particular series of trials involving pairs of two children and a mobile robot are described. The results show that the scenario with pairs of children and a robot creates a very interesting social context which gives rise to a variety of different social and non-social interaction patterns, demonstrating the specific problems but also abilities of children with autism in social interactions. Future work will include a closer analysis of interactional structure in human-human and robot-human interaction. We outline a particular framework that we are investigating.

Experiments into biology-technology interaction

In this paper a look is taken at how the use of implant and electrode technology can be employed to create biological brains for robots, to enable human enhancement and to diminish the effects of certain neural illnesses. In all cases the end result is to increase the range of abilities of the recipients. An indication is given of a number of areas in which such technology has already had a profound effect, a key element being the need for a clear interface linking a biological brain directly with computer technology. The emphasis is placed on practical scientific studies that have been and are being undertaken and reported on. The area of focus is the use of electrode technology, where either a connection is made directly with the cerebral cortex and/or nervous system or where implants into the human body are involved. The paper also considers robots that have biological brains in which human neurons can be employed as the sole thinking machine for a real world robot body.

The future of human-machine interaction: implant technology

In this paper a look is taken at how the use of implant and electrode technology can be employed to create biological brains for robots, to enable human enhancement and to diminish the effects of certain neural illnesses. In all cases the end result is to increase the range of abilities of the recipients. An indication is given of a number of areas in which such technology has already had a profound effect, a key element being the need for a clear interface linking a biological brain directly with computer technology. The emphasis is placed on practical scientific studies that have been and are being undertaken and reported on. The area of focus is the use of electrode technology, where either a connection is made directly with the cerebral cortex and/or nervous system or where implants into the human body are involved. The paper also considers robots that have biological brains in which human neurons can be employed as the sole thinking machine for a real world robot body.

Use of myoelectric signals to command mobile entertainment robot by disabled children: Design and control architecture

Severely disabled children have little chance of environmental and social exploration and discovery. This lack of interaction and independency may lead to an idea that they are unable to do anything by themselves. In an attempt to help children in this situation, educational robotics can offer and aid, once it can provide them a certain degree of independency in the exploration of environment. The system developed in this work allows the child to transmit the commands to a robot through myoelectric and movement sensors. The sensors are placed on the child's body so they can obtain information from the body inclination and muscle contraction, thus allowing commanding, through a wireless communication, the mobile entertainment robot to carry out tasks such as play with objects and draw. In this paper, the details of the robot design and control architecture are presented and discussed. With this system, disabled children get a better cognitive development and social interaction, balancing in a certain way, the negative effects of their disabilities. © 2012 IEEE.

Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Retraining of interjoint arm coordination after stroke using robot-assisted time-independent functional training

We have developed a haptic-based approach for retraining of interjoint coordination following stroke called time-independent functional training (TIFT) and implemented this mode in the ARMin III robotic exoskeleton. The ARMin III robot was developed by Drs. Robert Riener and Tobias Nef at the Swiss Federal Institute of Technology Zurich (Eidgenossische Technische Hochschule Zurich, or ETH Zurich), in Zurich, Switzerland. In the TIFT mode, the robot maintains arm movements within the proper kinematic trajectory via haptic walls at each joint. These arm movements focus training of interjoint coordination with highly intuitive real-time feedback of performance; arm movements advance within the trajectory only if their movement coordination is correct. In initial testing, 37 nondisabled subjects received a single session of learning of a complex pattern. Subjects were randomized to TIFT or visual demonstration or moved along with the robot as it moved though the pattern (time-dependent [TD] training). We examined visual demonstration to separate the effects of action observation on motor learning from the effects of the two haptic guidance methods. During these training trials, TIFT subjects reduced error and interaction forces between the robot and arm, while TD subject performance did not change. All groups showed significant learning of the trajectory during unassisted recall trials, but we observed no difference in learning between groups, possibly because this learning task is dominated by vision. Further testing in stroke populations is warranted.

Using the Own Flexibility of a Climbing Robot as a Double Force Sensor

Force sensors are used when interaction tasks are carried out by robots in general, and by climbing robots in particular. If the mechanics and electronics systems are contained inside the own robot, the robot becomes portable without external control. Commercial force sensors cannot be used due to limited space and weight. By selecting the links material with appropriate stiffness and placing strain gauges on the structure, the own robot flexibility can be used such as force sensor. Thus, forces applied on the robot tip can be measured without additional external devices. Only gauges and small internal electronic converters are necessary. This paper illustrates the proposed algorithm to achieve these measurements. Additionally, experimental results are presented.

Experimental tests in human-robot collision evaluation and characterization of a new safety index for robot operation

This paper describes an experimental procedure consisting of impact tests that simulate a collision of a human head with an industrial robot with the aim to validate a safety index named as New Index for Robots (NIR) and its outputs. The experiments in this paper are based on lab tests. It is an attempt to characterize the NIR index underlying the main parameters that are involved in crash interaction and to highlight limitations and weakness of suggested impact tests.

Modeling a mobile robot using a grammatical model

Virtual Worlds Generator is a grammatical model that is proposed to define virtual worlds. It integrates the diversity of sensors and interaction devices, multimodality and a virtual simulation system. Its grammar allows the definition and abstraction in symbols strings of the scenes of the virtual world, independently of the hardware that is used to represent the world or to interact with it. A case study is presented to explain how to use the proposed model to formalize a robot navigation system with multimodal perception and a hybrid control scheme of the robot.

A grammatical approach to the modeling of an autonomous robot

Virtual Worlds Generator is a grammatical model that is proposed to define virtual worlds. It integrates the diversity of sensors and interaction devices, multimodality and a virtual simulation system. Its grammar allows the definition and abstraction in symbols strings of the scenes of the virtual world, independently of the hardware that is used to represent the world or to interact with it. A case study is presented to explain how to use the proposed model to formalize a robot navigation system with multimodal perception and a hybrid control scheme of the robot. The result is an instance of the model grammar that implements the robotic system and is independent of the sensing devices used for perception and interaction. As a conclusion the Virtual Worlds Generator adds value in the simulation of virtual worlds since the definition can be done formally and independently of the peculiarities of the supporting devices.

3D Visual Data-Driven Spatiotemporal Deformations for Non-Rigid Object Grasping Using Robot Hands

Sensing techniques are important for solving problems of uncertainty inherent to intelligent grasping tasks. The main goal here is to present a visual sensing system based on range imaging technology for robot manipulation of non-rigid objects. Our proposal provides a suitable visual perception system of complex grasping tasks to support a robot controller when other sensor systems, such as tactile and force, are not able to obtain useful data relevant to the grasping manipulation task. In particular, a new visual approach based on RGBD data was implemented to help a robot controller carry out intelligent manipulation tasks with flexible objects. The proposed method supervises the interaction between the grasped object and the robot hand in order to avoid poor contact between the fingertips and an object when there is neither force nor pressure data. This new approach is also used to measure changes to the shape of an object’s surfaces and so allows us to find deformations caused by inappropriate pressure being applied by the hand’s fingers. Test was carried out for grasping tasks involving several flexible household objects with a multi-fingered robot hand working in real time. Our approach generates pulses from the deformation detection method and sends an event message to the robot controller when surface deformation is detected. In comparison with other methods, the obtained results reveal that our visual pipeline does not use deformations models of objects and materials, as well as the approach works well both planar and 3D household objects in real time. In addition, our method does not depend on the pose of the robot hand because the location of the reference system is computed from a recognition process of a pattern located place at the robot forearm. The presented experiments demonstrate that the proposed method accomplishes a good monitoring of grasping task with several objects and different grasping configurations in indoor environments.

Influence of predominant patterns of coordination on the exploitation of interaction torques in a two-joint rhythmic arm movement

In this study we investigate the coordination between rhythmic flexion-extension (FE) and supination-pronation (SP) movements at the elbow joint-complex, while manipulating the intersegmental dynamics by means of a 2-degrees of freedom (df) robot arm. We hypothesized that constraints imposed by the structure of the neuromuscular-skeletal system would (1) result in predominant pattern(s) of coordination in the absence of interaction torques and (2) influence the capabilities of participants to exploit artificially induced interaction torques. Two experiments were conducted in which different conditions of interaction torques were applied on the SP-axis as a function of FE movements. These conditions promoted different patterns of coordination between the 2-df. Control trials conducted in the absence of interaction torques revealed that both the in-phase (supination synchronized with flexion) and the anti-phase (pronation synchronized with flexion) patterns were spontaneously established by participants. The predominance of these patterns of coordination is explained in terms of the mechanical action of bi-articular muscles acting at the elbow joint-complex, and in terms of the reflexes that link the activity of the muscles involved. Results obtained in the different conditions of interaction torques revealed that those neuromuscular-skeletal constraints either impede or favor the exploitation of intersegmental dynamics depending on the context. Interaction torques were indeed found to be exploited to a greater extent in conditions in which the profiles of interaction torques favored one of the two predominant patterns of coordination (i.e., in-phase or anti-phase) as opposed to other patterns of coordination (e.g., 90 degrees or 270 degrees). Those results are discussed in relation to recent studies reporting exploitation of interaction torques in the context of rhythmic movements.

Multimodal attention system for an interactive robot

[EN]Social robots are receiving much interest in the robotics community. The most important goal for such robots lies in their interaction capabilities. An attention system is crucial, both as a filter to center the robot’s perceptual resources and as a mean of letting the observer know that the robot has intentionality. In this paper a simple but flexible and functional attentional model is described. The model, which has been implemented in an interactive robot currently under development, fuses both visual and auditive information extracted from the robot’s environment, and can incorporate knowledge-based influences on attention.

Téléopération d'un robot collaboratif par outil haptique

Ce projet de recherche, intitulé Téléopération d'un robot collaboratif par outil haptique traite un des problèmes contemporains de la robotique, à savoir la coopération entre l'humain et la machine. La robotique est en pleine expansion depuis maintenant deux décennies: les robots investissent de plus en plus l'industrie, les services ou encore l'assistance à la personne et se diversifient considérablement. Ces nouvelles tendances font sortir les robots des cages dans lesquelles ils étaient placés et ouvrent grand la porte vers de nouvelles applications. Parmi elles, la coopération et les interactions avec l'humain représentent une réelle opportunité pour soulager l'homme dans des tâches complexes, fastidieuses et répétitives. En parallèle de cela, la robotique moderne s'oriente vers un développement massif du domaine humanoïde. Effectivement, plusieurs expériences sociales ont montré que l'être humain, constamment en interaction avec les systèmes qui l'entourent, a plus de facilités à contribuer à la réalisation d'une tâche avec un robot d'apparence humaine plutôt qu'avec une machine. Le travail présenté dans ce projet de recherche s'intègre dans un contexte d'interaction homme-robot (IHR) qui repose sur la robotique humanoïde. Le système qui en découle doit permettre à un utilisateur d'interagir efficacement et de façon intuitive avec la machine, tout en respectant certains critères, notamment de sécurité. Par une mise en commun des compétences respectives de l'homme et du robot humanoïde, les interactions sont améliorées. En effet, le robot peut réaliser une grande quantité d'actions avec précision et sans se fatiguer, mais n'est pas nécessairement doté d'une prise de décision adaptée à la situation, contrairement à l'homme qui est capable d'ajuster son comportement naturellement ou en fonction de son expérience. En d'autres termes, ce système cherche à intégrer le savoir-faire et la capacité de réflexion humaine avec la robustesse, l'efficacité et la précision du robot. Dans le domaine de la robotique, le terme d'interaction intègre également la notion de contrôle. La grande majorité des robots reçoit des commandes machines qui sont généralement des consignes de trajectoire, qu'ils sont capables d'interpréter. Or, plusieurs interfaces de contrôle sont envisageables, notamment celles utilisant des outils haptiques, qui permettent à un utilisateur d'avoir un ressenti et une perception tactile. Ces outils comme tous ceux qui augmentent le degré de contrôle auprès de l'utilisateur, en ajoutant un volet sensoriel, sont parfaitement adaptés pour ce genre d'applications. Dans ce projet, deux outils haptiques sont assemblés puis intégrés à une interface de contrôle haptique dans le but de commander le bras d'un robot humanoïde. Ainsi, l'homme est capable de diriger le robot tout en ajustant ses commandes en fonction des informations en provenance des différents capteurs du robot, qui lui sont retranscrites visuellement ou sensoriellement.

Safe and Collaborative Navigation & Interaction With Mobile Manipulators in Domestic Appliance Test Labs

Safe collaboration between a robot and human operator forms a critical requirement for deploying a robotic system into a manufacturing and testing environment. In this dissertation, the safety requirement for is developed and implemented for the navigation system of the mobile manipulators. A methodology for human-robot co-existence through a 3d scene analysis is also investigated. The proposed approach exploits the advance in computing capability by relying on graphic processing units (GPU’s) for volumetric predictive human-robot contact checking. Apart from guaranteeing safety of operators, human-robot collaboration is also fundamental when cooperative activities are required, as in appliance test automation floor. To achieve this, a generalized hierarchical task controller scheme for collision avoidance is developed. This allows the robotic arm to safely approach and inspect the interior of the appliance without collision during the testing procedure. The unpredictable presence of the operators also forms dynamic obstacle that changes very fast, thereby requiring a quick reaction from the robot side. In this aspect, a GPU-accelarated distance field is computed to speed up reaction time to avoid collision between human operator and the robot. An automated appliance testing also involves robotized laundry loading and unloading during life cycle testing. This task involves Laundry detection, grasp pose estimation and manipulation in a container, inside the drum and during recovery grasping. A wrinkle and blob detection algorithms for grasp pose estimation are developed and grasp poses are calculated along the wrinkle and blobs to efficiently perform grasping task. By ranking the estimated laundry grasp poses according to a predefined cost function, the robotic arm attempt to grasp poses that are more comfortable from the robot kinematic side as well as collision free on the appliance side. This is achieved through appliance detection and full-model registration and collision free trajectory execution using online collision avoidance.