PGA: Personalizing Grasping Agents with Single Human-Robot Interaction

Language-Conditioned Robotic Grasping (LCRG) aims to develop robots that ground and grasp objects based on natural language instructions. While robots capable of recognizing personal objects like "my wallet" can interact more naturally with non-expert users, current LCRG systems primarily limit robots to understanding only generic expressions. To this end, we introduce a task scenario GraspMine with a novel dataset that aims to locate and grasp personal objects given personal indicators via learning from a single human-robot interaction. To address GraspMine, we propose Personalized Grasping Agent (PGA), that learns personal objects by propagating user-given information through a Reminiscence-a collection of raw images from the user's environment. Specifically, PGA acquires personal object information by a user presenting a personal object with its associated indicator, followed by PGA inspecting the object by rotating it. Based on the acquired information, PGA pseudo-labels objects in the Reminiscence by our proposed label propagation algorithm. Harnessing the information acquired from the interactions and the pseudo-labeled objects in the Reminiscence, PGA adapts the object grounding model to grasp personal objects. Experiments on GraspMine show that PGA significantly outperforms baseline methods both in offline and online settings, signifying its effectiveness and personalization applicability on real-world scenarios. Finally, qualitative analysis shows the effectiveness of PGA through a detailed investigation of results in each phase.Comment: 7 pages, under revie

A field study on Polish customers' attitude towards a service robot in a cafe

More and more stores in Poland are adopting robots as customer assistants or promotional tools. However, customer attitudes to such novelty remain unexplored. This study focused on the role of social robots in self-service cafes. This domain has not been explored in Poland before, and there is not much research in other countries as well. We conducted a field study in two cafes with a teleoperated robot Nao, which sat next to the counter serving as an assistant to a human barista. We observed customer behavior, conducted semi-structured interviews and questionnaires with the customers. The results show that Polish customers are neutral and insecure about robots. However, they do not exhibit a total dislike of these technologies. We considered three stages of the interaction and identified features of each stage that need to be designed carefully to yield user satisfaction.Comment: 14 pages, 1 figur

Towards long-term social child-robot interaction: using multi-activity switching to engage young users

Social robots have the potential to provide support in a number of practical domains, such as learning and behaviour change. This potential is particularly relevant for children, who have proven receptive to interactions with social robots. To reach learning and therapeutic goals, a number of issues need to be investigated, notably the design of an effective child-robot interaction (cHRI) to ensure the child remains engaged in the relationship and that educational goals are met. Typically, current cHRI research experiments focus on a single type of interaction activity (e.g. a game). However, these can suffer from a lack of adaptation to the child, or from an increasingly repetitive nature of the activity and interaction. In this paper, we motivate and propose a practicable solution to this issue: an adaptive robot able to switch between multiple activities within single interactions. We describe a system that embodies this idea, and present a case study in which diabetic children collaboratively learn with the robot about various aspects of managing their condition. We demonstrate the ability of our system to induce a varied interaction and show the potential of this approach both as an educational tool and as a research method for long-term cHRI

Acoustic Features of Different Types of Laughter in North Sami Conversational Speech

Peer reviewe

Investigating Human Perceptions of Trust and Social Cues in Robots for Safe Human-Robot Interaction in Human-oriented Environments

As robots increasingly take part in daily living activities, humans will have to interact with them in domestic and other human-oriented environments. This thesis envisages a future where autonomous robots could be used as home companions to assist and collaborate with their human partners in unstructured environments without the support of any roboticist or expert. To realise such a vision, it is important to identify which factors (e.g. trust, participants’ personalities and background etc.) that influence people to accept robots’ as companions and trust the robots to look after their well-being. I am particularly interested in the possibility of robots using social behaviours and natural communications as a repair mechanism to positively influence humans’ sense of trust and companionship towards the robots. The main reason being that trust can change over time due to different factors (e.g. perceived erroneous robot behaviours). In this thesis, I provide guidelines for a robot to regain human trust by adopting certain human-like behaviours. I can expect that domestic robots will exhibit occasional mechanical, programming or functional errors, as occurs with any other electrical consumer devices. For example, these might include software errors, dropping objects due to gripper malfunctions, picking up the wrong object or showing faulty navigational skills due to unclear camera images or noisy laser scanner data respectively. It is therefore important for a domestic robot to have acceptable interactive behaviour when exhibiting and recovering from an error situation. In this context, several open questions need to be addressed regarding both individuals’ perceptions of the errors and robots, and the effects of these on people’s trust in robots. As a first step, I investigated how the severity of the consequences and the timing of a robot’s different types of erroneous behaviours during an interaction may have different impact on users’ attitudes towards a domestic robot. I concluded that there is a correlation between the magnitude of an error performed by the robot and the corresponding loss of trust of the human in the robot. In particular, people’s trust was strongly affected by robot errors that had severe consequences. This led us to investigate whether people’s awareness of robots’ functionalities may affect their trust in a robot. I found that people’s acceptance and trust in the robot may be affected by their knowledge of the robot’s capabilities and its limitations differently according the participants’ age and the robot’s embodiment. In order to deploy robots in the wild, strategies for mitigating and re-gaining people’s trust in robots in case of errors needs to be implemented. In the following three studies, I assessed if a robot with awareness of human social conventions would increase people’s trust in the robot. My findings showed that people almost blindly trusted a social and a non-social robot in scenarios with non-severe error consequences. In contrast, people that interacted with a social robot did not trust its suggestions in a scenario with a higher risk outcome. Finally, I investigated the effects of robots’ errors on people’s trust of a robot over time. The findings showed that participants’ judgement of a robot is formed during the first stage of their interaction. Therefore, people are more inclined to lose trust in a robot if it makes big errors at the beginning of the interaction. The findings from the Human-Robot Interaction experiments presented in this thesis will contribute to an advanced understanding of the trust dynamics between humans and robots for a long-lasting and successful collaboration

Distributed Dynamic Hierarchical Task Assignment for Human-Robot Teams

This work implements a joint task architecture for human-robot collaborative task execution using a hierarchical task planner. This architecture allowed humans and robots to work together as teammates in the same environment while following several task constraints. These constraints are 1) sequential order, 2) non-sequential, and 3) alternative execution constraints. Both the robot and the human are aware of each other's current state and allocate their next task based on the task tree. On-table tasks, such as setting up a tea table or playing a color sequence matching game, validate the task architecture. The robot will have an updated task representation of its human teammate's task. Using this knowledge, it is also able to continuously detect the human teammate's intention towards each sub-task and coordinate it with the teammate. While performing a joint task, there can be situations in which tasks overlap or do not overlap. We designed a dialogue-based conversation between humans and robots to resolve conflict in the case of overlapping tasks.Evaluating the human-robot task architecture is the next concern after validating the task architecture. Trust and trustworthiness are some of the most critical metrics to explore. A study was conducted between humans and robots to create a homophily situation. Homophily means when a person feels biased towards another person because of having similarities in social ways. We conducted this study to determine whether humans can form a homophilic relationship with robots and whether there is a connection between homophily and trust. We found a correlation between homophily and trust in human-robot interactions.Furthermore, we designed a pipeline by which the robot learns a task by observing the human teammate's hand movement while conversing. The robot then constructs the tree by itself using a GA learning framework. Thus removing the need for manual specification by a programmer each time to revise or update the task tree which makes the architecture more flexible, realistic, efficient, and dynamic. Additionally, our architecture allows the robot to comprehend the context of a situation by conversing with a human teammate and observing the surroundings. The robot can find a link between the context of the situation and the surrounding objects by using the ontology approach and can perform the desired task accordingly. Therefore, we proposed a human-robot distributed joint task management architecture that addresses design, improvement, and evaluation under multiple constraints

ON THE INFLUENCE OF SOCIAL ROBOTS IN COGNITIVE MULTITASKING AND ITS APPLICATION

[Objective] I clarify the impact of social robots on cognitive tasks, such as driving a car or driving an airplane, and show the possibility of industrial applications based on the principles of social robotics. [Approach] I adopted the MATB, a generalized version of the automobile and airplane operation tasks, as cognitive tasks to evaluate participants' performance on reaction speed, tracking performance, and short-term memory tasks that are widely applicable, rather than tasks specific to a particular situation. Also, as the stimuli from social robots, we used the iCub robot, which has been widely used in social communication research. In the analysis of participants, I not only analyzed performance, but also mental workload using skin conductance and emotional analysis of arousal-valence using facial expressions analysis. In the first experiment, I compared a social robot that use social signals with a nonsocial robot that do not use such signals and evaluated whether social robots affect cognitive task performances. In the second experiment, I focused on vitality forms and compared a calm social robot with an assertive social robot. As analysis methods, I adopted Mann-Whitney's U test for one-pair comparisons, and ART-ANOVA for analysis of variance in repeated task comparisons. Based on the results, I aimed to express vitality forms in a robot head, which is smaller in size and more flexible in placement than a full-body humanoid robot, considering car and airplane cockpit's limited space. For that, I developed a novel eyebrow and I decided to use a wire-driven technique, which is widely used in surgical robots to control soft materials. [Main results] In cognitive tasks such as car drivers and airplane pilots, I clarified the effects of social robots acting social behaviors on task performance, mental workload, and emotions. In addition, I focused on vitality forms, one of the parameters of social behaviors, and clarified the effects of different vitality forms of social robots' behavior on cognitive tasks.In cognitive tasks such as car drivers and airplane pilots, we clarified the effects of social robots acting in social behaviors on task performance, mental workload, and emotions, and showed that the presence of social robots can be effective in cognitive tasks. Furthermore, focusing on vitality forms, one of the parameters of social behaviors, we clarified the effects of different vitality forms of social robots' behaviors on cognitive tasks, and found that social robots with calm behaviors positively affected participants' facial expressions and improved their performance in a short-term memory task. Based on the results, I decided to adopt the configuration of a robot head, eliminating the torso from the social humanoid robot, iCub, considering the possibility of placement in a limited space such as cockpits of car or airplane. In designing the robot head, I developed a novel soft-material eyebrow that can be mounted on the iCub robot head to achieve continuous position and velocity changes, which is an important factor to express vitality forms. The novel eyebrows can express different vitality forms by changing the shape and velocity of the eyebrows, which was conventionally represented by the iCub's torso and arms. [Significance] The results of my research are important achievements that opens up the possibility of applying social robots to non-robotic industries such as automotive and aircraft. In addition, the newly developed soft-material eyebrows' precise shape and velocity changes have opened up new research possibilities in social robotics and social communication research themselves, enabling experiments with complex facial expressions that move beyond Ekman's simple facial expression changes definition, such as, joy, anger, sadness, and pleasure. Thus, the results of this research are one important step in both scientific and industrial applications. [Key-words] social robot, cognitive task, vitality form, robot head, facial expression, eyebro