Recent technological advances in hardware design of the robotic platforms enabled the implementation of various control modalities for improved interactions with humans and unstructured environments. ...An important application area for the integration of robots with such advanced interaction capabilities is human–robot collaboration. This aspect represents high socio-economic impacts and maintains the sense of purpose of the involved people, as the robots do not completely replace the humans from the work process. The research community’s recent surge of interest in this area has been devoted to the implementation of various methodologies to achieve intuitive and seamless human–robot-environment interactions by incorporating the collaborative partners’ superior capabilities, e.g. human’s cognitive and robot’s physical power generation capacity. In fact, the main purpose of this paper is to review the state-of-the-art on intermediate human–robot interfaces (bi-directional), robot control modalities, system stability, benchmarking and relevant use cases, and to extend views on the required future developments in the realm of human–robot collaboration.
Improving work conditions in industry is a major challenge that can be addressed with new emerging technologies such as collaborative robots. Machine learning techniques can improve the performance ...of those robots, by endowing them with a degree of awareness of the human state and ergonomics condition. The availability of appropriate datasets to learn models and test prediction and control algorithms, however, remains an issue. This article presents a dataset of human motions in industry-like activities, fully labeled according to the ergonomics assessment worksheet EAWS, widely used in industries such as car manufacturing. Thirteen participants performed several series of activities, such as screwing and manipulating loads under different conditions, resulting in more than 5 hours of data. The dataset contains the participants’ whole-body kinematics recorded both with wearable inertial sensors and marker-based optical motion capture, finger pressure force, video recordings, and annotations by three independent annotators of the performed action and the adopted posture following the EAWS postural grid. Sensor data are available in different formats to facilitate their reuse. The dataset is intended for use by researchers developing algorithms for classifying, predicting, or evaluating human motion in industrial settings, as well as researchers developing collaborative robotics solutions that aim at improving the workers’ ergonomics. The annotation of the whole dataset following an ergonomics standard makes it valuable for ergonomics-related applications, but we expect its use to be broader in the robotics, machine learning, and human movement communities.
To investigate the dynamics of human-robot acceptance, we carried out an experimental study with 56 adult participants and the iCub robot. Trust in the robot has been considered as a main indicator ...of acceptance and measured by the participants' conformation to the iCub's answers to questions on functional and social tasks characterized by perceptual and socio cognitive uncertainty. In particular, we were interested in understanding whether (i) trust in functional savvy is a prerequisite for trust in social savvy, and (ii) to what extent factors such as participants' desire for control, attitude towards social influence of robots, and imagined collaborative vs. competitive scenario, may influence their trust in the iCub. We found that participants conformed more to the iCub's answers in the functional than in the social tasks. Moreover, the few participants conforming to the iCub's answers in the social task also conformed less in the functional issues. Trust in the robot's functional savvy does not thus seem to be a pre-requisite for trust in its social savvy. Finally, the examined factors did not influence the trust in iCub. Results are discussed with relation to methodology of human-robot interaction (HRI) research.
•We investigate the functional and social acceptance of the iCub robot.•Trust in the robot is considered as a main indicator of acceptance in decision-making tasks.•The participants trust more iCub for functional than for social issues.•Trust in the robot's functional savvy does not seem to be a pre-requisite for trust in its social savvy.•User-related, robot-related and context-related features are also evaluated: they do not seem to influence the trust in iCub.
Overhead work is a frequent cause of shoulder work-related musculoskeletal disorders. Exoskeletons offering arm support have the potential to reduce shoulder strain, without requiring large scale ...reorganization of the workspace. Assessment of such systems however requires to take multiple factors into consideration. This paper presents a thorough in-lab assessment of PAEXO, a novel passive exoskeleton for arm support during overhead work. A list of evaluation criteria and associated performance metrics is proposed to cover both objective and subjective effects of the exoskeleton, on the user and on the task being performed. These metrics are measured during a lab study, where 12 participants perform an overhead pointing task with and without the exoskeleton, while their physical, physiological and psychological states are monitored. Results show that using PAEXO reduces shoulder physical strain as well as global physiological strain, without increasing low back strain nor degrading balance. These positive effects are achieved without degrading task performance. Importantly, participants' opinions of PAEXO are positive, in agreement with the objective measures. Thus, PAEXO seems a promising solution to help prevent shoulder injuries and diseases among overhead workers, without negatively impacting productivity.
In this paper, we tackle the problem of multimodal learning for autonomous robots. Autonomous robots interacting with humans in an evolving environment need the ability to acquire knowledge from ...their multiple perceptual channels in an unsupervised way. Most of the approaches in the literature exploit engineered methods to process each perceptual modality. In contrast, robots should be able to acquire their own features from the raw sensors, leveraging the information elicited by interaction with their environment: learning from their sensorimotor experience would result in a more efficient strategy in a life-long perspective. To this end, we propose an architecture based on deep networks, which is used by the humanoid robot iCub to learn a task from multiple perceptual modalities (proprioception, vision, audition). By structuring high-dimensional, multimodal information into a set of distinct sub-manifolds in a fully unsupervised way, it performs a substantial dimensionality reduction by providing both a symbolic representation of data and a fine discrimination between two similar stimuli. Moreover, the proposed network is able to exploit multimodal correlations to improve the representation of each modality alone.
•We propose a fully unsupervised algorithm for perception.•The algorithm processes high-dimensional, multimodal input.•The output is a symbolic representation along with continuous traits.•We apply it on a robotic task involving vision, proprioception and speech.
Evaluating the Engagement with Social Robots Anzalone, Salvatore M.; Boucenna, Sofiane; Ivaldi, Serena ...
International journal of social robotics,
08/2015, Volume:
7, Issue:
4
Journal Article
Peer reviewed
Open access
To interact and cooperate with humans in their daily-life activities, robots should exhibit human-like “intelligence”. This skill will substantially emerge from the interconnection of all the ...algorithms used to ensure cognitive and interaction capabilities. While new robotics technologies allow us to extend such abilities, their evaluation for social interaction is still challenging. The quality of a human–robot interaction can not be reduced to the evaluation of the employed algorithms: we should integrate the engagement information that naturally arises during interaction in response to the robot’s behaviors. In this paper we want to show a practical approach to evaluate the engagement aroused during interactions between humans and social robots. We will introduce a set of metrics useful in direct, face to face scenarios, based on the behaviors analysis of the human partners. We will show how such metrics are useful to assess how the robot is perceived by humans and how this perception changes according to the behaviors shown by the social robot. We discuss experimental results obtained in two human-interaction studies, with the robots Nao and iCub respectively.
Improving the ergonomy of working environments is essential to reducing work-related musculo-skeletal disorders. We consider real-time ergonomic feedback a key technology for achieving such ...improvements. To this end, we present supportive tools for online evaluation and visualization of strenuous efforts and postures of a worker, also when physically interacting with a robot. A digital human model is used to estimate human kinematics and dynamics and visualize non-ergonomic joint angles, based on the on-line data acquired from a wearable motion tracking device.
Learning visual predictive models has great potential for real-world robot manipulations. Visual predictive models serve as a model of real-world dynamics to comprehend the interactions between the ...robot and objects. However, prior works in the literature have focused mainly on low-level elementary robot actions, which typically result in lengthy, inefficient, and highly complex robot manipulation. In contrast, humans usually employ top–down thinking of high-level actions rather than bottom–up stacking of low-level ones. To address this limitation, we present a novel formulation for robot manipulation that can be accomplished by pick-and-place, a commonly applied high-level robot action, through grasping. We propose a novel visual predictive model that combines an action decomposer and a video prediction network to learn the intrinsic semantic information of high-level actions. Experiments show that our model can accurately predict the object dynamics (i.e., the object movements under robot manipulation) while trained directly on observations of high-level pick-and-place actions. We also demonstrate that, together with a sampling-based planner, our model achieves a higher success rate using high-level actions on a variety of real robot manipulation tasks.
Estimating the engagement is critical for human–robot interaction. Engagement measures typically rely on the dynamics of the social signals exchanged by the partners, especially speech and gaze. ...However, the dynamics of these signals are likely to be influenced by individual and social factors, such as personality traits, as it is well documented that they critically influence how two humans interact with each other. Here, we assess the influence of two factors, namely extroversion and negative attitude toward robots, on speech and gaze during a cooperative task, where a human must physically manipulate a robot to assemble an object. We evaluate if the score of extroversion and negative attitude towards robots co-variate with the duration and frequency of gaze and speech cues. The experiments were carried out with the humanoid robot iCub and N = 56 adult participants. We found that the more people are extrovert, the more and longer they tend to talk with the robot; and the more people have a negative attitude towards robots, the less they will look at the robot face and the more they will look at the robot hands where the assembly and the contacts occur. Our results confirm and provide evidence that the engagement models classically used in human–robot interaction should take into account attitudes and personality traits.
One of the most important challenges in robotics is to achieve natural and engaging interactions between humans and robots. Interpersonal interaction, the process by which two or more people exchange ...information through verbal and nonverbal messages, could be exploited by robots both to establish interaction and to inform about its quality. The production of social signals being influenced by individual factors, such as personality traits, is critical for robots to have personalized models of interaction. This paper focuses on estimating social traits such as human personality from the dynamics of interpersonal interaction. The work was carried out in the framework of the project engagement during human–humanoid interaction which focuses on understanding individual factors influencing joint human–humanoid actions. In particular, this work addresses the automated prediction of the Extraversion trait during a human–humanoid interaction. We show how it is possible to take into account the specificity of human–robot interaction scenarios by contextualizing personality through the attitude that participants show towards the robots. This attitude is influenced by the a priori knowledge that people have on social robotics, and their prior anxiety in interacting with them. The proposed model exploits a set of nonverbal features chosen according to literature in Psychology and Personality Computing. These features are adopted to characterize human behaviors and the dynamics of human–robot interaction. Experimental results highlight that it possible to predict Extraversion of the human partner from
nonverbal
behavior
during human–robot interaction
with an accuracy of 62%. A higher accuracy, 70%, is obtained from the computational model by explicitly combining the dynamics of interpersonal interaction and the attitude of the participants towards the robots. Results show that 120 s are needed to obtain such performance.
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