To design trustworthy robots, we need to understand the impact factors of trust: people's attitudes, experience, and characteristics; the robot's physical design, reliability, and performance; a task's specification and the circumstances under which it is to be performed, e.g. at leisure or under time pressure. As robots are used for a wide variety of tasks and applications, robot designers ought to be provided with evidence and guidance, to inform their decisions to achieve safe, trustworthy and efficient human-robot interactions. In this work, the impact factors of trust in a collaborative manufacturing scenario are studied by conducting an experiment with a real robot and participants where a physical object was assembled and then disassembled. Objective and subjective measures were employed to evaluate the development of trust, under faulty and non-faulty robot conditions, and the effect of previous experience with robots, and personality traits. Our findings highlight differences when compared to other, more social, scenarios with robotic assistants (such as a home care assistant), in that the condition (faulty or not) does not have a significant impact on the human's perception of the robot in terms of human-likeliness, likeability, trustworthiness, and even competence. However, personality and previous experience do have an effect on how the robot is perceived by participants, even though that is relatively small.
Perceived characteristics of a robot including competence, responsibility, and credibility (and combinations of these), have been proposed to provide means for evaluating the level of trust in a machine @cite_24 @cite_11 . Still, a clear stance on what exactly is being measured is necessary @cite_35 (e.g. trust in terms of competence as in @cite_24 , or trustworthiness' directly, as in @cite_9 ). Measuring trust in human-robot interactions is challenging. A momentary state of trust can be captured in an experiment, but trust is earned over a longer term @cite_11 @cite_30 @cite_9 . Accumulation of small errors might affect trust more than a single large error, and a poor initial performance by a robot could leave a stronger negative impact @cite_24 . Also, trust is not only a situational construct (i.e. depending on the situation), but it is also dispositional (i.e. depends on the individual and its characteristics) @cite_30 . Additionally, the study of human-robot interactions through experiments has many practical and ethical issues and associated limitations, as participants should not be exposed to dangerous situations, nor should they be deceived @cite_34 .
@cite_30 @cite_35 @cite_9 @cite_24 @cite_34 @cite_11
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Prior work in human trust of autonomous robots suggests the timing of reliability drops impact trust and control allocation strategies. However, trust is traditionally measured post-run, thereby masking the real-time changes in trust, reducing sensitivity to factors like inertia, and subjecting the measure to biases like the primacy-recency effect. Likewise, little is known on how feedback of robot confidence interacts in real-time with trust and control allocation strategies. An experiment to examine these issues showed trust loss due to early reliability drops is masked in traditional post-run measures, trust demonstrates inertia, and feedback alters allocation strategies independent of trust. The implications of specific findings on development of trust models and robot design are also discussed. We describe a model of “trust” in human-robot systems that is inferred from their interactions, and inspired by similar concepts relating to trust among humans. This computable quantity allows a robot to estimate the extent to which its performance is consistent with a human's expectations, with respect to task demands. Our trust model drives an adaptive mechanism that dynamically adjusts the robot's autonomous behaviors, in order to improve the efficiency of the collaborative team. We illustrate this trust-driven methodology through an interactive visual robot navigation system. This system is evaluated through controlled user experiments and a field demonstration using an aerial robot. The ability to perceive humans is an essential requirement for safe and efficient human-robot interaction. In real-world applications, the need for a robot to interact in real time with multiple humans in a dynamic, 3-D environment presents a significant challenge. The recent availability of commercial color-depth cameras allow for the creation of a system that makes use of the depth dimension, thus enabling a robot to observe its environment and perceive in the 3-D space. Here we present a system for 3-D multiple human perception in real time from a moving robot equipped with a color-depth camera and a consumer-grade computer. Our approach reduces computation time to achieve real-time performance through a unique combination of new ideas and established techniques. We remove the ground and ceiling planes from the 3-D point cloud input to separate candidate point clusters. We introduce the novel information concept, depth of interest, which we use to identify candidates for detection, and that avoids the computationally expensive scanning-window methods of other approaches. We utilize a cascade of detectors to distinguish humans from objects, in which we make intelligent reuse of intermediary features in successive detectors to improve computation. Because of the high computational cost of some methods, we represent our candidate tracking algorithm with a decision directed acyclic graph, which allows us to use the most computationally intense techniques only where necessary. We detail the successful implementation of our novel approach on a mobile robot and examine its performance in scenarios with real-world challenges, including occlusion, robot motion, nonupright humans, humans leaving and reentering the field of view (i.e., the reidentification challenge), human-object and human-human interaction. We conclude with the observation that the incorporation of the depth information, together with the use of modern techniques in new ways, we are able to create an accurate system for real-time 3-D perception of humans by a mobile robot. Recognition of intentions is a subconscious cognitive process vital to human communication. This skill enables anticipation and increases the quality of interactions between humans. Within the context of engagement, non-verbal signals are used to communicate the intention of starting the interaction with a partner. In this paper, we investigated methods to detect these signals in order to allow a robot to know when it is about to be addressed. Originality of our approach resides in taking inspiration from social and cognitive sciences to perform our perception task. We investigate meaningful features, i.e. human readable features, and elicit which of these are important for recognizing someone's intention of starting an interaction. Classically, spatial information like the human position and speed, the human-robot distance are used to detect the engagement. Our approach integrates multimodal features gathered using a companion robot equipped with a Kinect. The evaluation on our corpus collected in spontaneous conditions highlights its robustness and validates the use of such a technique in a real environment. Experimental validation shows that multimodal features set gives better precision and recall than using only spatial and speed features. We also demonstrate that 7 selected features are sufficient to provide a good starting engagement detection score. In our last investigation, we show that among our full 99 features set, the space reduction is not a solved task. This result opens new researches perspectives on multimodal engagement detection. Multimodal approach for starting engagement detection using non-explicit cues.Results show that our approach performs better than spatial one in all conditions.MRMR strategy reduces the features space to 7 features without a performance loss.Validation of Schegloff (sociologist) meaningful features for engagement detection.A robot centered labeled corpus of 4 hours in a home-like environment. In all human-robot interaction, trust is an important element to consider because the presence or absence of trust certainly impacts the ultimate outcome of that interaction. Limited research exists that delineates the development and maintenance of this trust in various operational contexts. Our own prior research has investigated theoretical and empirically supported antecedents of human-robot trust. Here, we describe progress to date relating to the development of a comprehensive human-robot trust model based on our ongoing program of research. The concept of affordances, introduced in psychology by J. J. Gibson, has recently attracted interest in the development of cognitive systems in autonomous robotics. In earlier work (Sahin, Cakmak, Dogar, Ugur, & Acoluk), we reviewed the uses of this concept in different fields and proposed a formalism to use affordances at different levels of robot control. In this article, we first review studies in ecological psychology on the learning and perception of traversability in organisms and describe how the existence of traversability was judged to exist. We then describe the implementation of one part of the affordance formalism for the learning and perception of traversability affordances on a mobile robot equipped with range sensing ability. Through experiments inspired by ecological psychology, we show that the robot, by interacting with its environment, can learn to perceive the traversability affordances. Moreover, we claim that three of the main attributes that are commonly associated with affordances, that is, affordances being relative to the environment, providing perceptual economy, and providing general information, are simply consequences of learning from the interactions of the robot with the environment. As robots enter the everyday physical world of people, it is important that they abide by society's unspoken social rules such as respecting people's personal spaces. In this paper, we explore issues related to human personal space around robots, beginning with a review of the existing literature in human-robot interaction regarding the dimensions of people, robots, and contexts that influence human-robot interactions. We then present several research hypotheses which we tested in a controlled experiment (N=30). Using a 2 (robotics experience vs. none: between-participants) × 2 (robot head oriented toward a participant's face vs. legs: within-participants) mixed design experiment, we explored the factors that influence proxemic behavior around robots in several situations: (1) people approaching a robot, (2) people being approached by an autonomously moving robot, and (3) people being approached by a teleoperated robot. We found that personal experience with pets and robots decreases a person's personal space around robots. In addition, when the robot's head is oriented toward the person's face, it increases the minimum comfortable distance for women, but decreases the minimum comfortable distance for men. We also found that the personality trait of agreeableness decreases personal spaces when people approach robots, while the personality trait of neuroticism and having negative attitudes toward robots increase personal spaces when robots approach people. These results have implications for both human-robot interaction theory and design. Shared expectations and mutual understanding are critical facets of teamwork. Achieving these in human-robot collaborative contexts can be especially challenging, as humans and robots are unlikely to share a common language to convey intentions, plans, or justifications. Even in cases where human co-workers can inspect a robot's control code, and particularly when statistical methods are used to encode control policies, there is no guarantee that meaningful insights into a robot's behavior can be derived or that a human will be able to efficiently isolate the behaviors relevant to the interaction. We present a series of algorithms and an accompanying system that enables robots to autonomously synthesize policy descriptions and respond to both general and targeted queries by human collaborators. We demonstrate applicability to a variety of robot controller types including those that utilize conditional logic, tabular reinforcement learning, and deep reinforcement learning, synthesizing informative policy descriptions for collaborators and facilitating fault diagnosis by non-experts. In this paper, we present a framework for guiding autonomous learning in robot systems. The paradigm we introduce allows a robot to acquire new skills according to an intrinsic motivation function that finds behavioral affordances. Affordances-in the sense of (Gibson, Toward and Ecological Psychology, Hillsdale, NJ, 1977)-describe the latent possibilities for action in the environment and provide a direct means of organizing functional knowledge in embodied systems. We begin by showing how a robot can assemble closed-loop action primitives from its sensory and motor resources, and then show how these primitives can be sequenced into multi-objective policies. We then show how these policies can be assembled hierarchically to support incremental and cumulative learning. The main contribution of this paper demonstrates how the proposed intrinsic motivator for affordance discovery can cause a robot to both acquire such hierarchical policies using reinforcement learning and then to generalize these policies to new contexts. As the framework is described, its effectiveness and applicability is demonstrated through a longitudinal learning experiment on a bimanual robot. To increase human trust in robots, we have developed a system that provides insight into robotic behaviors by enabling a robot to answer questions people pose about its actions (e.g., Q: “Why did you turn left there?” A: “I detected a person at the end of the hallway.”). Our focus is on generation of this explanation in human-understandable terms despite the mathematical, robot-specific representation and planning system used by the robot to make its decisions and execute its actions. We present our work to date on this topic, including system design and experiments, and discuss areas for future work. The use of autonomous, mobile professional service robots in diverse workplaces is expected to grow substantially over the next decade. These robots often will work side by side with people, collaborating with employees on tasks. Some roboticists have argued that, in these cases, people will collaborate more naturally and easily with humanoid robots as compared with machine-like robots. It is also speculated that people will rely on and share responsibility more readily with robots that are in a position of authority. This study sought to clarify the effects of robot appearance and relative status on human-robot collaboration by investigating the extent to which people relied on and ceded responsibility to a robot coworker. In this study, a 3 × 3 experiment was conducted with human likeness (human, human-like robot, and machine-like robot) and status (subordinate, peer, and supervisor) as dimensions. As far as we know, this study is one of the first experiments examining how people respond to robotic coworkers. As such, this study attempts to design a robust and transferable sorting and assembly task that capitalizes on the types of tasks robots are expected to do and is embedded in a realistic scenario in which the participant and confederate are interdependent. The results show that participants retained more responsibility for the successful completion of the task when working with a machine-like as compared with a humanoid robot, especially when the machine-like robot was subordinate. These findings suggest that humanoid robots may be appropriate for settings in which people have to delegate responsibility to these robots or when the task is too demanding for people to do, and when complacency is not a major concern. Machine-like robots, however, may be more appropriate when robots are expected to be unreliable, are less well-equipped for the task than people are, or in other situations in which personal responsibility should be emphasized. We consider the problem of navigating a mobile robot through dense human crowds. We begin by exploring a fundamental impediment to classical motion planning algorithms called the âfreezing robot problemâ: once the environment surpasses a certain level of dynamic complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place or performs unnecessary maneuvers to avoid collisions. We argue that this problem can be avoided if the robot anticipates human cooperation, and accordingly we develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a âmultiple goalâ extension that models the goal-driven nature of human decision making. We validate this model with an empirical study of robot navigation in dense human crowds 488 runs, specifically testing how cooperation models effect navigation performance. The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 0.8 humans m2, while a state-of-the-art non-cooperative planner exhibits unsafe behavior more than three times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people m2. We also show that our non-cooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds. A major bottleneck in the realization of autonomous robotic agents performing complex manipulation tasks are the requirements that these tasks impose onto perception mechanisms. There is a strong need to scale robot perception capabilities along two dimensions: First, the variations of appearances and perceptual properties that real-world objects exhibit. Second, the variety of perceptual tasks, like categorizing and localizing, decomposing objects into their functional parts, perceiving the affordances they provide. This paper, addresses this need by organizing perception into a two-stage process. First, a pervasive and 'calm' perceptual component runs continually and interprets the incoming image stream to form a general purpose hybrid (symbolic sub-symbolic) belief state. This is used by the second component, the task-directed perception subsystem, to perform the respective perception tasks in a more informed way. We describe and discuss the first component and explain how it can manage realistic belief states, form a memory of past perceptual experiences, and compute valuable perceptual attributes without delaying plan execution. It does so by exploiting that perception is not a one-shot task but rather a secondary task that is pervasively and calmly performed throughout the lifetime of the robot. We show system operating on a leading-edge manipulation platform. An important ability of a robot that interacts with the environment and manipulates objects is to deal with the uncertainty in sensory data. Sensory information is necessary to, for example, perform online assessment of grasp stability. We present methods to assess grasp stability based on haptic data and machine-learning methods, including AdaBoost, support vector machines (SVMs), and hidden Markov models (HMMs). In particular, we study the effect of different sensory streams to grasp stability. This includes object information such as shape; grasp information such as approach vector; tactile measurements from fingertips; and joint configuration of the hand. Sensory knowledge affects the success of the grasping process both in the planning stage (before a grasp is executed) and during the execution of the grasp (closed-loop online control). In this paper, we study both of these aspects. We propose a probabilistic learning framework to assess grasp stability and demonstrate that knowledge about grasp stability can be inferred using information from tactile sensors. Experiments on both simulated and real data are shown. The results indicate that the idea to exploit the learning approach is applicable in realistic scenarios, which opens a number of interesting venues for the future research. When humans and mobile robots share the same space, one of their challenges is to navigate around each other and manage their mutual navigational intents. While humans have developed excellent skills in inferring their counterpart's intentions via a number of implicit and non-verbal cues, making navigation also in crowds an ease, this kind of effective and efficient communication often falls short in human-robot encounters. In this paper, two alternative approaches to convey navigational intent of a mobile robot to humans in a shared environment are proposed and analysed. The first is utilising anthropomorphic features of the mobile robot to realise an implicit joint attention using gaze to represent the direction of navigational intent. In the second approach, a more technical design adopting the semantics of car's turn indicators, has been implemented. The paper compares both approaches with each other and against a control behaviour without any communication of intent. Both approaches show statistically significant differences in comparison to the control behaviour. However, the second approach using indicators has shown as being more effective in conveying the intent and also has a higher positive impact on the comfort of the humans encountering the robot. Affect sensitivity is of the utmost importance for a robot companion to be able to display socially intelligent behaviour, a key requirement for sustaining long-term interactions with humans. This paper explores a naturalistic scenario in which children play chess with the iCat, a robot companion. A person-independent, Bayesian approach to detect the user's engagement with the iCat robot is presented. Our framework models both causes and effects of engagement: features related to the user's non-verbal behaviour, the task and the companion's affective reactions are identified to predict the children's level of engagement. An experiment was carried out to train and validate our model. Results show that our approach based on multimodal integration of task and social interaction-based features outperforms those based solely on non-verbal behaviour or contextual information (94.79 vs. 93.75 and 78.13 ). There has been a recent paradigm shift in robotics to data-driven learning for planning and control. Due to large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success failure. However, in most cases, these sensors provide weak supervision at best. In this work, we propose an adversarial learning framework that pits an adversary against the robot learning the task. In an effort to defeat the adversary, the original robot learns to perform the task with more robustness leading to overall improved performance. We show that this adversarial framework forces the the robot to learn a better grasping model in order to overcome the adversary. By grasping 82 of presented novel objects compared to 68 without an adversary, we demonstrate the utility of creating adversaries. We also demonstrate via experiments that having robots in adversarial setting might be a better learning strategy as compared to having collaborative multiple robots. Two experiments are reported which examined operators' trust in and use of the automation in a simulated supervisory process control task. Tests of the integrated model of human trust in machines proposed by Muir (1994) showed that models of interpersonal trust capture some important aspects of the nature and dynamics of human-machine trust. Results showed that operators' subjective ratings of trust in the automation were based mainly upon their perception of its competence. Trust was significantly reduced by any sign of incompetence in the automation, even one which had no effect on overall system performance. Operators' trust changed very little with experience, with a few notable exceptions. Distrust in one function of an automatic component spread to reduce trust in another function of the same component, but did not generalize to another independent automatic component in the same system, or to other systems. There was a high positive correlation between operators' trust in and use of the automation;... In situated human-robot dialogue, although humans and robots are co-present in a shared environment, they have significantly mismatched capabilities in perceiving the shared environment. Their representations of the shared world are misaligned. In order for humans and robots to communicate with each other successfully using language, it is important for them to mediate such differences and to establish common ground. To address this issue, this paper describes a dialogue system that aims to mediate a shared perceptual basis during human-robot dialogue. In particular, we present an empirical study that examines the role of the robot’s collaborative effort and the performance of natural language processing modules in dialogue grounding. Our empirical results indicate that in situated human-robot dialogue, a low collaborative effort from the robot may lead its human partner to believe a common ground is established. However, such beliefs may not reflect true mutual understanding. To support truly grounded dialogues, the robot should make an extra effort by making its partner aware of its internal representation of the shared world. It is natural for humans to work with abstract plans which are often an intuitive and concise way to represent a task. However, high level task descriptions contain symbols and concepts which need to be grounded within the environment if the plan is to be executed by an autonomous robot. The problem of learning the mapping between abstract plan symbols and their physical instances in the environment is known as the problem of physical symbol grounding. In this paper, we propose a framework for Grounding and Learning Instances through Demonstration and Eye tracking (GLIDE). We associate traces of task demonstration to a sequence of fixations which we call fixation programs and exploit their properties in order to perform physical symbol grounding. We formulate the problem as a probabilistic generative model and present an algorithm for computationally feasible inference over the proposed model. A key aspect of our work is that we estimate fixation locations within the environment which enables the appearance of symbol instances to be learnt. Instance learning is a crucial ability when the robot does not have any knowledge about the model or the appearance of the symbols referred to in the plan instructions. We have conducted human experiments and demonstrate that GLIDE successfully grounds plan symbols and learns the appearance of their instances, thus enabling robots to autonomously execute tasks in initially unknown environments.