1,720,983 research outputs found
Personality- and Memory-Based Software Framework for Human-Robot Interaction
No full textThe synergic orchestration of the cognitive and psychological dimensions characterizes human intelligence. Accordingly, carefully designing this mechanism in artificial intelligence can be a successful strategy to increase human likeness in a robot, enhancing mutual understanding and building a more natural and intuitive interaction. For this purpose, the main contribution of this work is a psychological and cognitive architecture tailored for HRI based on the interplay between robotic personality and memory-based cognitive processes. Indeed, the artificial personality manifests itself not only in various aspects of the behavior but also within the action selection process, which is closely intertwined with personality-dependent hedonic experiences linked to memories. Within this paper, we propose a task- and platform-independent framework, evaluated in a multiparty collaborative scenario. Obtained results show that a robot connected to our proposed framework is perceived as a cognitive agent capable of manifesting perceivable and distinguishable personality traits
Robot-Induced Group Conversation Dynamics: A Model to Balance Participation and Unify Communities
No full textThe purpose of this research is to study the impact of robot participation in group conversations and assess the effectiveness of different addressing policies. The study involved a total of 300 participants, who were divided into groups of four and engaged in a dialogue with a humanoid robot. The robot acted as a moderator, using information obtained during the conversation to determine which speaker to address. The study found that the policy used by the robot significantly impacted the conversation dynamics. Specifically, the robot provided more balanced attention to each participant and reduced the number of subgroups
Strategies for Controlling the Conversation Dynamics in Multi-Party Human-Robot Interaction
No full textThis article tackles the research question of whether it is possible to control conversation dynamics in a multi-party scenario using easily implementable solutions on off-the-shelf robotic platforms. To this end, we expanded upon our previously developed cloud robotic architecture by incorporating policies aimed at managing conversation dynamics through selective addressing of individuals, with the ultimate goal of balancing or unbalancing users’ participation or making subgroups of participants interact. Specifically, we computed the dominance of each speaker as a weighted sum of their speaking time and the number of words spoken within a moving window and used the Louvain algorithm to partition speakers into a set of non-overlapping communities. We then implemented six control policies, which were applied by the robot. Two of them, named BH and BS, aim to reduce dominance error (i.e., the difference in dominance between the most and least dominant speakers—both policies give the floor to the less dominant speaker). Two other policies, UH and US, are designed to increase the dominance error (both give the floor to the most dominant speaker). Finally, CH and CS aim to reduce the community error (i.e., the difference between the actual number of detected subgroups among speakers and the ideal target of a single group to which all speakers belong). Policies BH, UH, and CH (with “H” standing for “hard”) do not allow any exceptions to the policy rules, while BS, US, and CS (with “S” for “soft”) permit exceptions. To test the impact of these policies, we conducted a between-subjects study (N = 300) involving middle school students engaging in dialogue with a humanoid robot acting as a moderator. The study compared five conditions: in four of them, the robot used information gathered during the conversation to decide which speaker to address, applying one of the control policies—BH, BS, CH, or CS. The policies UH and US were excluded, as having a robot consistently give the floor to the most dominant child may raise ethical concerns. In the fifth condition, a baseline neutral policy (N) was applied, in which the robot did not explicitly address any speaker. The results imply that a robot using the proper control policies can influence conversation dynamics to keep both dominance error and community error significantly lower than those of a robot using the baseline policy, leading to more balanced participation and a reduction in the number of subgroups. Indeed, statistically significant differences have been found between the five policies considered in the dominance and community errors. However, no statistically significant differences in user experience—as measured by three scales of the validated SASSI questionnaire—were found when the robot used one of the control policies, as compared to the baseline, suggesting that participants are not negatively impacted by the robot’s attempt to control the conversation
Enhancing LLM-Based Human-Robot Interaction with Nuances for Diversity Awareness
No full textThis paper presents a system for diversity-aware autonomous conversation leveraging the capabilities of large language models (LLMs). The system adapts to diverse populations and individuals, considering factors like background, personality, age, gender, and culture. The conversation flow is guided by the structure of the system's pre-established knowledge base, while LLMs are tasked with various functions, including generating diversity-aware sentences. Achieving diversity-awareness involves providing carefully crafted prompts to the models, incorporating comprehensive information about users, conversation history, contextual details, and specific guidelines. To assess the system's performance, we conducted both controlled and real-world experiments, measuring a wide range of performance indicators
Machiavellian Robots and Their Theory of Mind
No full textThe objective of this work is to develop and evaluate computational cognitive models of Theory of Mind (ToM) and Machiavellian behavior embedded in a humanoid robot. Machiavellianism, together with psychopathy and narcissism, is part of the Dark Triad (DT), three constructs that correspond to socially aversive yet not necessarily pathological personalities. The motivations of the present work are both theoretical and application-oriented. In the long term, we aim to: (i) Provide researchers with new insights into the Machiavellian as well as other DT constructs through simulated and robotic setups; (ii) Provide a tool to train psychologists to deal with social and antisocial behavior in a controlled setup; (iii) Help people become aware of the behavioral mechanisms that they may expect from people with DT traits in social and affective relationships; (iv) Assist robotic engineers in developing better robots by identifying behaviors that should be avoided. To this end, we explored a computational model of ToM in the popular Planning Domain Definition Language (PDDL), and defined a domain with the necessary elements to induce Machiavellian behavior during planning and execution. Subsequently, we implemented our computational model in a software architecture controlling the behavior of a humanoid robot and recorded videos of the robot interacting with two actors. Finally, we conducted experiments with 300 participants divided into 6 conditions to verify whether the implemented framework is versatile enough to generate behaviors that participants would rate as either more Machiavellian or less Machiavellian based on their observations of the recorded videos
Development of a fully autonomous culturally competent robot companion
No full textThis chapter addresses the problem of producing fully autonomous, intelligent robots: that is, robots equipped with sensors, actuators, and Artificial Intelligence programs allowing them to perceive the environment, make decisions, and act without being teleoperated or following a script. First, we provide a brief review of today's robotic technology by discussing why most robots shown in the media should be viewed as outstanding examples of mechanics and control that, however, are neither intelligent nor autonomous. Then, we follow the steps from creating a prototype in a research lab to deploying a robot operating 24/7 in the real world: we discuss the technological challenges and propose smart solutions to achieve the biggest impact with the slightest effort. Finally, we illustrate some ideas for the future
Towards a Framework for the Whole-Body Teleoperation of a Humanoid Robot in Healthcare Settings
No full textThe use of robotic systems for doctor-patient interaction during Covid-19 and in post-pandemic phases has been proven useful. On the other hand, in current implementations, teleoperating a robot in critical contexts such as the medical scenario may induce a high mental workload on the operator, mainly due to the need to adapt to the remote control of a complex robot, and the reduced environmental awareness. Furthermore, robotic platforms for telemedicine do not usually offer the possibility of establishing physical contact with the patient, which may indeed be useful to show how to assume a certain posture, or to guide a specific movement. The aim of this work is to overcome these limitations, by creating a framework in which the arms, the head, and the base of a humanoid robot can be easily teleoperated with a rapid learning curve and a low mental workload for the operator. The proposed approach is based on the real-time human pose estimation of the operator, which is calculated in real-time and transformed into correspondent skeleton joint angles, used as input to control the upper body joints of the Softbank Robotics robot Pepper. Experiments with users have been performed to check the effectiveness of the imitation system, by verifying the similarity between the human and robot pose and measuring its usability and perceived workload
Grounding Conversational Robots on Vision Through Dense Captioning and Large Language Models
No full textThis work explores a novel approach to empowering robots with visual perception capabilities using textual descriptions. Our approach involves the integration of GPT-4 with dense captioning, enabling robots to perceive and interpret the visual world through detailed text-based descriptions. To assess both user experience and the technical feasibility of this approach, experiments were conducted with human participants interacting with a Pepper robot equipped with visual capabilities. The results affirm the viability of the proposed approach, allowing to perform vision-based conversations effectively, despite processing time limitations
T.P.T. a novel Taekwondo personal trainer robot
No full textIn recent years, robotics has been widely used in the sport sector, but few examples of robotic platforms are currently used in combat sports. This work presents T.P.T., a novel robotic prototype used in the context of Taekwondo, an Olympic martial art sport, able of interacting with children and with adult athletes. In this paper, the conceptual and functional design of the robot, including some preliminary tests aimed at its calibration, is described in details. The robot has been presented at the 2013 Italian Championship of Taekwondo, and it is in a patent pending status (Muscolo and Recchiuto, 2013)
- …
