Robots inhabiting human environments need to act in relation to their own experience and embodiment as well as to social and emotional aspects. Robots that learn, act upon and incorporate their own experience and perception of others’ emotions into their responses make not only more productive artificial agents but also agents with whom humans can appropriately interact. This special issue seeks to address the significance of grounding of emotions in robots in relation to aspects of physical and homeostatic interaction in the world at an individual and social level. Specific questions concern: How can emotion and social interaction be grounded in the behavioral activity of the robotic system? Is a robot able to have intrinsic emotions? How can emotions, grounded in the embodiment of the robot, facilitate individually and socially adaptive behavior to the robot? This opening chapter provides an introduction to the articles that comprise this special issue and briefly discusses their relationship to grounding emotions in robots.@en

As robots become more ubiquitous, they will increasingly need to behave as our team partners and smoothly adapt to the (adaptive) human team behaviors to establish successful patterns of collaboration over time. A substantial amount of adaptations present themselves through subtle and unconscious interactions, which are difficult to observe. Our research aims to bring about awareness of co-adaptation that enables team learning. This paper presents an experimental paradigm that uses a physical human-robot collaborative task environment to explore emergent human-robot co-adaptions and derive the interaction patterns (i.e., the targeted awareness of co-adaptation). The paradigm provides a tangible human-robot interaction (i.e., a leash) that facilitates the expression of unconscious adaptations, such as “leading” (e.g., pulling the leash) and “following” (e.g., letting go of the leash) in a search-and-navigation task. The task was executed by 18 participants, after which we systematically annotated videos of their behavior. We discovered that their interactions could be described by four types of adaptive interactions: stable situations, sudden adaptations, gradual adaptations and active negotiations. From these types of interactions we have created a language of interaction patterns that can be used to describe tacit co-adaptation in human-robot collaborative contexts. This language can be used to enable communication between collaborating humans and robots in future studies, to let them share what they learned and support them in becoming aware of their implicit adaptations.@en

The aim of this study is to understand users' experience and their perceived privacy, while interacting with a crowd-operated social robot. We conducted a between-subjects user study, wherein the robot broadcasts both audio and video to crowd workers in one condition, as opposed to broadcasting only the participants' audio cues in the other condition. A sample of 14 students took part in this study, and was divided into 2 groups (video and No-Video group). Participants were asked to use the help of a crowd-operated Pepper robot to find their next holiday destination. Once the interaction was completed, participants assessed the social intelligence, user experience and privacy aspects of the robot in both conditions. No significant differences were experienced by participants regarding social intelligence and user experience across both conditions. Interestingly, less privacy was perceived by the group with an audio-only broadcast feed compared to the audio-video feed.@en

We present Crowd of Oz (CoZ) - a crowd powered system to enable real time dialogue between a Pepper robot and a user. CoZ includes media rich interfaces and advanced architecture to swiftly elicit crowd responses for the Pepper robot. We have empirically demonstrated that users can be engaged in a beneficial discussion with the crowd-powered robot, that acts as a coach to mitigate stress.@en

Coping with stress is crucial for a healthy lifestyle. In the past, a great deal of research has been conducted to use socially assistive robots as a therapy to alleviate stress and anxiety related problems. However, building a fully autonomous social robot which can deliver psycho-therapeutic solutions is a very challenging endeavor due to limitations in artificial intelligence (AI). To overcome AI’s limitations, researchers have previously introduced crowdsourcing-based teleoperation methods, which summon the crowd’s input to control a robot’s functions. However, in the context of robotics, such methods have only been used to support the object manipulation, navigational, and training tasks. It is not yet known how to leverage real-time crowdsourcing (RTC) to process complex therapeutic conversational tasks for social robotics. To fill this gap, we developed Crowd of Oz (CoZ), an open-source system that allows Softbank’s Pepper robot to support such conversational tasks. To demonstrate the potential implications of this crowd-powered approach, we investigated how effectively, crowd workers recruited in real-time can teleoperate the robot’s speech, in situations when the robot needs to act as a life coach. We systematically varied the number of workers who simultaneously handle the speech of the robot (N = 1, 2, 4, 8) and investigated the concomitant effects for enabling RTC for social robotics. Additionally, we present Pavilion, a novel and open-source algorithm for managing the workers’ queue so that a required number of workers are engaged or waiting. Based on our findings, we discuss salient parameters that such crowd-powered systems must adhere to, so as to enhance their performance in response latency and dialogue quality.@en