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In this paper we discuss how people understand robots when they imagine relations with such technologies in near-future settings. We argue that imagination forms an important part of the assemblage of robotic technologies, because it is central in how people relate to, understand and feel about robotic bodies, behaviours and intent. As we will show, the terms in which people imagine robots as relational emerge from the contexts of their activities, their potential effects on people, and also what people already know and feel about these and other technologies. We discuss insights from a study that asked research participants to discuss whether robots ‘feel right’ in different public space settings. Building on growing scholarship in robot geographies, we show how imaginative responses to robots are located in dynamic relational configurations that draw together the contexts, appearance, behaviours and perceived intentions of robots, and people’s existing understandings of technology to understand what robots might or could do. An imaginative account of robot geographies offers interdisciplinary value by positing new frameworks for how robots are understood relationally, and in terms of possibility, that can contribute to studies in social robotics.

Rapid advances in digital technologies have allowed robots to become more autonomous and efficacious than ever before. Future developments in robotics hold the potential to transform human robot in...

In an effort towards the democratization of Robotics, this article presents a novel End-User Development framework called Robot Interfaces From Zero Experience (RIZE). The framework provides a set of useful software tools for the creation of robot-oriented software architectures and programming interfaces, as well as the modeling and execution of robot behaviors, with a specific emphasis on social behaviors. Programming interfaces built on top of RIZE enable professionals with different backgrounds and interests to design, adapt, and scale-up robotics applications. As an example of a programming interface, we present Open RIZE, which exploits an End-User Programming paradigm combining blocks, tables, and forms-filling interfaces. Unlike previous approaches, robot behavioral code generated by Open RIZE is intrinsically modular, re-usable, scalable, neutral to the employed programming language, and platform-agnostic. In the article, we present the main design guidelines and features of Open RIZE. Additionally, we perform an initial usability evaluation of the Open RIZE interface in an online workshop. Preliminary results using the System Usability Scale with 10 novice end-users indicate that Open RIZE is easy-to-use and learn.

Researchers in biomechanics, neuroscience, human–machine interaction and other fields are interested in inferring human intentions and objectives from observed actions. The problem of inferring objectives from observations has received extensive theoretical and methodological development from both the controls and machine learning communities. In this paper, we provide an integrating view of objective learning from human demonstration data. We differentiate algorithms based on the assumptions made about the objective function structure, how the similarity between the inferred objectives and the observed demonstrations is assessed, the assumptions made about the agent and environment model, and the properties of the observed human demonstrations. We review the application domains and validation approaches of existing works and identify the key open challenges and limitations. The paper concludes with an identification of promising directions for future work.

People are increasingly encountering robots in public spaces. To increase the robustness of such in-the-wild robotic applications and to achieve their designed outcomes, existing research focuses on improving the technical reliability of robots and identifying effective strategies to prevent or recover from technical failures. However, in human-robot interaction (HRI), a user’s perception of a robot failure may not necessarily relate to technical issues. We focus on understanding users’ perception of robot behaviours and interactions within the context of a public space. In our exploratory study using a novel participatory design methodology, participants designed robot behaviours for applications in public spaces, and tested their design both in a simulator and on the physical robot. We investigate how participants’ perception and expectations change during this iterative participatory prototyping process, especially when the robot exhibits erratic or unexpected behaviours. Our work provides insights on users’ perception of robot failures, and how users’ design of robot behaviours shifts as they observe the robot within the spatial and social context.

When a robot learns from human examples, most approaches assume that the human partner provides examples of optimal behavior. However, there are applications in which the robot learns from non-expert humans. We argue that the robot should learn not only about the human's objectives, but also about their expertise level. The robot could then leverage this joint information to reduce or increase the frequency at which it provides assistance to its human's partner or be more cautious when learning new skills from novice users. Similarly, by taking into account the human's expertise, the robot would also be able of inferring a human's true objectives even when the human's fails to properly demonstrate these objectives due to a lack of expertise. In this paper, we propose to jointly infer the expertise level and objective function of a human given observations of their (possibly) non-optimal demonstrations. Two inference approaches are proposed. In the first approach, inference is done over a finite, discrete set of possible objective functions and expertise levels. In the second approach, the robot optimizes over the space of all possible hypotheses and finds the objective function and expertise level that best explain the observed human behavior. We demonstrate our proposed approaches both in simulation and with real user data., 17 pages, TRO Submission

Human-Robot Interaction (HRI) user studies are challenging to evaluate and compare due to a lack of standardization and the infrastructure required to implement each study. The lack of experimental infrastructure also makes it difficult to systematically evaluate the impact of individual components (e.g., the quality of perception software) on overall system performance. This work proposes a framework to ease the implementation and reproducibility of human-robot interaction user studies. The framework utilizes ROS middleware and is implemented with four modules: perception, decision, action, and metrics. The perception module aggregates sensor data to be used by the decision and action modules. The decision module is the task-level executive and can be designed by the HRI researcher for their specific task. The action module takes subtask requests from the decision module and breaks them down into motion primitives for execution on the robot. The metrics module tracks and generates quantitative metrics for the study. The framework is implemented with modular interfaces to allow for alternate implementations within each module and can be generalized for a variety of tasks and human/robot roles. The framework is illustrated through an example scenario involving a human and a Franka Emika Panda arm collaboratively assembling a toolbox together.

People are increasingly encountering robots in public spaces, but they rarely have input into how such technology is conceived or deployed. To familiarise and engage the general public with new robot technology, we present a novel co-design workshop methodology with a three-phased structure: open discussion, participatory prototyping, and follow-up interviews. Open discussion collects users’ initial expectations; Participatory prototyping deepens users’ understanding of the technology and elicits concrete, user-centered discussion on the technology being investigated; and follow-up interviews collect personal reflections. An online workshop following this methodology facilitated our research on users’ expectations of robots in public spaces. Our observations highlighted that co-design workshops are effective for advancing users’ understanding of robotic technology and applications.

Estimating a user’s expertise level based on observations of their actions will result in better human-robot collaboration, by enabling the robot to adjust its behaviour and the assistance it provides according to the skills of the particular user it’s interacting with. This paper details an approach to incrementally and continually estimate the expertise of a user whose goal is to optimally complete a given task. The user’s expertise level, here represented as a scalar parameter, is estimated by evaluating how far their actions are from optimal. The proposed approach was tested using data from an online study where participants were asked to complete various instances of a simulated kitting task. An optimal planner was used to estimate the “goodness” of all available actions at any given task state. We found that our expertise level estimates correlate strongly with observed after-task performance metrics and that it is possible to differentiate novices from experts after observing, on average, 33% of the errors made by the novices.

For robots that collaborate alongside and work with humans, there is great interest in improving robot communication abilities to achieve engaging and successful interactions. Successful task collaborations between humans often involve functional motions in which implicit communication signals, such as affect, are embedded. Thus in order to improve a robot's communication capabilities, it is necessary to identify the different motor control strategies that humans employ when generating such implicit signals. This paper details the adaptation of an Inverse Optimal Control (IOC) methodology for this purpose. We hypothesize that IOC allows for the identification of the motion strategies involved in the implicit communication of affective content during the performance of functional movement. To test our hypothesis, a motion capture dataset consisting of upper-body functional movements was collected and annotated by multiple observers through a perceptual user study. Among the different control strategies considered during our analysis, we found that center of mass movement, quantity of motion, Laban space effort and effort were the most relevant when distinguishing motions that convey different affective states.

Endowing animated virtual characters with emotionally expressive behaviors is paramount to improving the quality of the interactions between humans and virtual characters. Full-body motion, in particular, with its subtle kinematic variations, represents an effective way of conveying emotionally expressive content. However, before synthesizing expressive full-body movements, it is necessary to identify and understand what qualities of human motion are salient to the perception of emotions and how these qualities can be exploited to generate novel and equally expressive full-body movements. Based on previous studies, we argue that it is possible to perceive and generate expressive full-body movements from a limited set of joint trajectories, including end-effector trajectories and additional constraints such as pelvis and elbow trajectories. Hence, these selected trajectories define a significant and reduced motion space, which is adequate for the characterization of the expressive qualities of human motion and that is both suitable for the analysis and generation of emotionally expressive full-body movements. The purpose and main contribution of this work is the methodological framework we defined and used to assess the validity and applicability of the selected trajectories for the perception and generation of expressive full-body movements. This framework consists of the creation of a motion capture database of expressive theatrical movements, the development of a motion synthesis system based on trajectories re-played or re-sampled and inverse kinematics, and two perceptual studies.

International audience; Virtual characters capable of showing emotionalcontent are considered as more believable and engaging. However,in spite of the numerous psychological studies and machinelearning applications trying to decode the most salient featuresin the expression and perception of affect, there is still nocommon understanding about how affect is conveyed throughbody motions. Based on findings reported by the psychologyresearch community and quantitative results obtained in thecomputer animation domain during the last years, we propose torepresent affective bodily movement through a low-dimensionalparameterization consisting of the spatio-temporal trajectories ofeight main joints in the human body (hands, head, feet, elbowsand pelvis). Using a combined evaluation protocol, we show thatthis low-dimensional parameterization and the features derivedfrom it are a compact and sufficient representation of affectivemotions that can be used for automatic recognition of affect andthe generation of new affective-expressive motions.

International audience; Designing and controlling virtual characters endowed with expressive gestures requires the modeling of multiple processes, involving high-level abstract representations to low-level sensorimotor models. An expressive gesture is here defined as a meaningful bodily motion which intrinsically associates sense, style, and expressiveness. The main challenges rely both on the capability to produce a large spectrum of parametrized actions executed with some variability in various situations, and on the biological plausibility of the motion of the virtual characters. The goals of the paper are twofold. First we review the different formalisms used to describe expressive gestures, from notations to computational languages. Secondly we identify and discuss remaining challenges in the generation of expressive virtual characters. The different models and formalisms are illustrated more particularly for theatrical and sign language gestures.

International audience; While human communication involves rich, complex and expressive gestures, available corpora of captured motions used for the animation of virtual characters contain actions ranging from locomotion to everyday life motions. We aim at creating a novel corpus of expressive and meaningful gestures, and we focus on body movements and gestures involved in theatrical scenarios. In this paper we propose a methodology for building a corpus of full-body theatrical gestures based on a magician show enriched with affective content. We then validate the constructed corpus of theatrical gestures and sequences through several perceptual studies focusing on the complexity of the produced movements as well as the recognizability of the additional affective content.