Researchers have been developing Social robots are increasingly common in schools to support algorithms to aid robots in determining task hierarchies learning goals, in workplaces to augment productivity, (Hayes and Scassellati 2014), learning tasks from humans and in homes to improve quality of life. The fulfillment of (Thomaz and Breazeal 2008), and choosing what information their objectives in these environments are strongly dependent to communicate and when to communicate it (Unhelkar on the quality of the sustained, supportive relationship and Shah 2016). Although robots have made great robots are able to construct with their human users.

Dialog between drivers and speech-based robot vehicle interfaces can be used as an instrument to find out what drivers might be concerned, confused or curious about in driving simulator studies. Eliciting ongoing conversation with drivers about topics that go beyond navigation, control of entertainment systems, or other traditional driving related tasks is important to getting drivers to engage with the activity in an open-ended fashion. In a structured improvisational Wizard of Oz study that took place in a highly immersive driving simulator, we engaged participant drivers (N=6) in an autonomous driving course where the vehicle spoke to drivers using computer-generated natural language speech. First, using microanalyses of drivers’ responses to the car’s utterances, we identify a set of topics that are expected and treated as appropriate by the participants in our study. Second, we identify a set of topics and conversational strategies that are treated as inappropriate. Third, we show that it is just these unexpected, inappropriate utterances that eventually increase users’ trust into the system, make them more at ease, and raise the system’s acceptability as a communication partner.

Based on the development of several different HRI scenarios using different robots, we have been establishing the SERA ecosystem. SERA is composed of both a model and tools for integrating an AI agent with a robotic embodiment, in humanrobot interaction scenarios. We present the model, and several of the reusable tools that were developed, namely Thalamus, Skene and Nutty Tracks. Finally we exemplify how such tools and model have been used and integrated in five different HRI scenarios using the NAO, Keepon and EMYS robots. Figure 1: Our methodology as an intersection of CGI animation, Human-robot interaction (HRI) systems are spreading as a IVA and robotics techniques.

Methods and materials are described for employing a human-shaped robot as a lecturer in automated remote instruction. Video segments from the stimuli of a 2 (participant substrate: VR or non-VR) x 2 (robot embodiment: copresent or screen) balanced between-participants experiment are provided. In each condition, a robot delivers the content for a lecture on the nutrition of carbohydrates. The robot uses identical speech and body movement while the same set of slides plays on an adjacent computer, thereby controlling for such factors as educational content, robot appearance and robot size. The experiment employs Aldebaran’s 25-degrees-of-freedom Nao as the robot and the Oculus Rift as the immersive VR system. The lecture speech and slides were obtained with permission from a Mandarin Chinese-language online course and translated into English. The setup for different delivery modes for automated remote instruction are illustrated using a robot delivering foreign language online content. These methods support the design and evaluation of robots that perform the role of lecturer.

When designing a robot to interact with people, the decision to incorporate a robot arm may arise. In this paper, we investigate adding an inexpensive, functional arm to our mobile CoBot service robots. Specifically, we examine two-dimensional extendable pantograph arms for CoBot. Pantograph arms have intuitive kinematics and inverse kinematics. Pantograph arms are modular and adding additional linkages corresponds to simple changes in the kinematic calculations. These arms have several advantages (and disadvantages) compared to traditional robot arms. A prototype pantograph arm is currently in development and our goal is to attach a modular pantograph arm to CoBot to perform simple needed tasks, such as knocking on doors and pressing elevator buttons.

This short paper describes experiences in the development of several smart power wheelchair platforms across three different sites. In the course of the project, we have re-used several of the components (both hardware and software) despite differences in the base platform of the robots. We describe the different platforms, and discuss some of the challenges and results of our work.

This work explores possible applications of electromagnetic platform stabilization (EPS) in systems suffering from vibration and resonance along multiple axes. Many HRI platforms are coupled rigid-body platforms; an EPS system acts as a non-rigid coupling between the mobile base and the upper-body structure. Electromagnets have many qualities that have been shown to perform well in complex mechanical systems; our initial investigations illustrate the utility of EPS technologies for reducing resonance in mobile robot platforms, particularly in human-robot interactions, in which sensors in the upper-body structure must be stable to track human users.

The increasing mere presence of robots in everyday life does not automatically result in gradual acceptance of these systems by human users. Over the past years, we have conducted several studies with the goal to provide insight into the long-term process of social robots in domestic environments. This paper presents our overall conclusions from the combined findings of our multiple studies on social robot acceptance. We will provide insights from a user’s perspective of what makes robots social, describe a phased framework of the long-term process of robot acceptance, present some key factors for social robot acceptance, offer guidelines to build better sociable robots, and provide some recommendations for conducting research in domestic environments. With sharing our experiences with conducting (long-term) user studies in domestic environments, we aim to serve to push this sub-field of HRI in real-world contexts forward and thereby the community at large.

Several ongoing research projects in Human autonomous car interactions are addressing the problem of safe co-existence for human and robot drivers on road. Automation in cars can vary across a continuum of levels at which it can replace manual tasks. Social relationships like anthropomorphic behavior of owners towards their cars is also expected to vary according to this spectrum of autonomous decision making capacity. Some researchers have proposed a joint cognitive model of a human-car collaboration that can make the best of the respective strengths of humans and machines. For a successful collaboration, it is important that the members of this human - car team develop, maintain and update each others behavioral models. We consider mutual trust as an integral part of these models. In this paper, we present a review of the quantitative models of trust in automation. We found that only a few models of humans’ trust on automation exist in literature that account for the dynamic nature of trust and may be leveraged in human car interaction. However, these models do not support mutual trust. Our review suggests that there is significant scope for future research in the domain of mutual trust modeling for human car interaction, especially, when considered over the lifetime of the vehicle. Hardware and computational framework (for sensing, data aggregation, processing and modeling) must be developed to support these adaptive models over the operational phase of autonomous vehicles. In order to further research in mutual human - automation trust, we propose a framework for integrating Mutual Trust compu- tation into standard Human - Robot Interaction research platforms. This framework includes User trust and Agent trust, the two fundamental components of Mutual trust. It allows us to harness multi-modal sensor data from the car as well as from the user’s wearable or handheld device. The proposed framework provides access to prior trust aggregate and other cars’ experience data from the Cloud and to feature primitives like gaze, facial expression, etc. from a standard low-cost Human - Robot Interaction platform.

There are many successful methods for transferring information from one agent to another. One approach, taken in this work, is to have one (source) agent demonstrate a policy to a second (target) agent, and then have that second agent improve upon the policy. By allowing the target agent to observe the source agent's demonstrations, rather than relying on other types of direct knowledge transfer like Q-values, rules, or shared representations, we remove the need for the agents to know anything about each other's internal representation or have a shared language. In this work, we introduce a refinement to HAT, an existing transfer learning method, by integrating the target agent's confidence in its representation of the source agent's policy. Results show that a target agent can effectively 1) improve its initial performance relative to learning without transfer (jumpstart) and 2) improve its performance relative to the source agent (total reward). Furthermore, both the jumpstart and total reward are improved with this new refinement, relative to learning without transfer and relative to learning with HAT.