Monocular 3D human pose estimation often faces challenges due to depth ambiguities, unstable predictions, and occlusions. Diffusion models have emerged as a generative framework that can transform noise into complex data representations. Although diffusion-based multi-hypothesis approaches have been explored in prior works, their effectiveness largely depends on the denoising network, which captures spatial and long-range temporal dependencies. In this paper, we propose MHDPose, a conditional diffusion-based human pose estimation framework that can generate multiple 3D pose predictions with the guidance of a single 2D pose. The pose hypotheses are generated by the proposed PMamba denoiser that consists of: (i) a spatial transformer over joints with proposed kinematic-aware rotary position embeddings (Kin-RoPE) to encode the skeleton’s tree structure, and (ii) bi-directional Mamba blocks for efficient long-range temporal modeling, and a residual Mamba head for pose refinement. Our proposed MHDPose achieves competitive results on widely used pose estimation benchmarks such as Human3.6M and MPI-INF-3DHP.

The integration of conversational agents into daily life has become increasingly common. However, sustaining deeply engaging and natural interactions remains challenging due to a lack of multimodal datasets capturing personal and emotional nuances. In this paper, we introduce MERCI (Multimodal dataset for Emotionally-aware peRsonalised Conversational In-teractions), a dataset derived from user-robot dialogues involving thirty participants who completed user profile questionnaires covering ten personal topics (e.g., hobbies, music). A conver-sational system called PERCY then engaged with each partici-pant in open-domain conversations, leveraging GPT-4, real-time facial-expression and sentiment analysis to generate contextu-ally appropriate, empathetic responses. MERCI contains 1860 utterances, equating to about 12.5 hours of aligned audio, three-view video, transcripts with timestamps, emotion labels, and sentiment scores. This dataset serves as a reproducible test-bed for tasks such as emotion-aware response generation, multimodal affect recognition, and personalised policy learning. Baseline performance results have been established using advanced models such as BERT, T5, BART, and GPT-3.5/4/4o-mini across gener-ation, regression, and classification. Evaluations through human and automated methods have demonstrated strong naturalness, relevance, and consistency in responses while indicating areas for enhanced personalisation and empathic depth. We expect that MERCI will enhance the development of emotionally intelligent, user-centric conversational AI applications, potentially ranging from social robotics to mental health support.

Human-robot interaction encompasses various applications, from simple tasks to advanced complex functionalities. This research introduces a specialised robotics end effector specially modelled with auditory processing for drumming along-side humans. This system interprets auditory vocal input to generate precise and adaptive drumming patterns. Utilising precision motor control and real-time signal processing to replicate drumming with human-like characteristics with precision. This work demonstrates collaboration between performing arts and robotics, demonstrating the potential of intelligent systems to interact with human creativity in real-time. The specialised end effector utilises precise motor control with motor drivers and a belt mechanism to regulate the speed and direction of the drumstick, enabling rapid, high-speed strokes to strike the drum 4 to 5 times per second. By integrating audio perception with mechanical execution, this research advances the state of the art and establishes a new framework for incorporating robotics into the creative and artistic domain.

Human-robot interaction plays a vital role in pushing the capabilities of socially interactive robots by enabling them to deliver content with high emotional intelligence. This research focuses on a supervisory fuzzy framework for constantly evaluating and improving the content delivered by the robot utilizing multimodal inputs and advanced intelligent algorithms. The main reason for using fuzzy logic is that it mimics human decision-making by providing a percentage-based measure of closeness. In this project, ARI Robot is being used with an LLM integration, which enables the user to communicate with the robot. Different algorithms were integrated for the classification of multimodal inputs, BERT (Bidirectional Encoder Representations from Transformers) for the classification of content, Wav2Vec 2.0 for classifying the tone of the user while interacting with the robot, and OpenFace for classifying the facial expression of the user. All of these inputs are then supervised by a fuzzy system with predefined rules to evaluate the content delivered and provide feedback for refinement. The proposed framework ensures an overall evaluation of content delivery, providing intelligent feedback to the ARI robot to improve interaction quality. By integrating these advanced models with fuzzy logic, the system mimics human-like judgment in assessing the interaction of verbal and non-verbal indications, making the way for emotionally intelligent robots in a social world.

Generative AI is transforming the way humans interact with robots by integrating language-driven comprehension with embodied execution. While recent research leveraging large language models (LLMs) to enhance communication between humans and machines has shown significant progress, the exploration of integrating LLMs with robots to assist users in creating real-world artistic works remains challenging. This research introduces a novel GENAI-driven intelligent robotic arm drawing system. We fine-tuned GPT-3.5 Turbo to better capture meaningful information from conversations with users and output precise drawing commands, which are directly fed into the fine-tuned Stable Diffusion model to generate desired images. A UFactory xArm equipped with an end-effector holding a drawing pen is deployed and controlled by a speed control algorithm to perform accurate drawing movements based on AI-generated images. Our proposed framework facilitates enhanced HRI experiences with a focus on drawing tasks, effectively embodying the capabilities of generative AI in the field of artistic creation and enabling users to engage deeply in the process of using AI for art creation in real-world environments.

Generative AI technologies are reshaping everyday environments by enabling multimodal interaction. As their ubiquity and agentic capacities grow, there is a pressing need to understand how these systems reshape human–computer interaction in relational, social, and systemic terms. We introduce a scenario-based design pack for investigating Human–GenAI relations. Grounded in assemblage theory and structured around a three-stage process—Prepare, Make, Reflect—the pack supports the prototyping, analysis, and critical reflection of emergent sociotechnical configurations. We evaluated the pack across three deployments: an ACM workshop (n=22), a multidisciplinary design session (n=20), and a university HCI class (n=260). Participants generated scenarios that surfaced relational issues of power, agency, visibility, and care. We contribute the design pack alongside an exploratory framework to advance relational enquiry into multimodal Human–GenAI relations, support more inclusive and socially responsive GenAI practices, and complement FATE approaches by grounding fairness, accountability, and transparency in lived, multimodal configurations.

This research explores an emerging phenomenon of perceived sentience in human-agent interaction (HAI) discourse where an anthropocentric bias is observed. We propose an alternative post-anthropocentric lens through developing a framework to help understand the perception of sentient entities as possible trans-objects. The term ’post-anthropocentric lens’ is coined to represent emerging future interactive objects where HAI may be imagined overlapping with post-human philosophy bridging the limitations of an anthropocentric design philosophy for HAI. Our proposal uses a research through design approach to explore possible future imaginings for object-object interaction among social agents and humans. We conclude that it is possible to define future emerging characteristics of autonomous agents through a decentralised post-anthropocentric approach towards HAI.

This research presents an exploration of a post-anthropocentric perspective towards the design of human-agent interaction (HAI), observed through the development of a wearable device prototype. The device argues for an alternative stance towards the orthodox human-centered perspectives in HAI executed through human-computer interaction (HCI). Taking a research through design approach the device presents a speculative scenario of agential interaction in the form of a perceptual companion interface. It overlaps with post-anthropocentric philosophical discourse around the phenomenon of agential perception present in contemporary imaginings of HCI. The prototype intends to act as a catalyst for imagining post-anthropocentric agential interactions within HAI.

The design and development of robots involve the essential step of selecting and testing robotic interfaces. This interface selection requires careful consideration as the robot’s physical embodiment influences and adds to the traditional interfaces’ complexities. Our paper presents a decision support design framework for the a priori selection of robotic interface that was inductively formulated from our case study of designing a robot to collaborate with employees with cognitive disabilities. Our main contribution is to provide a novel framework that outlines the interface requirements according to user, robot, tasks and environment and facilitates a structured comparison of interfaces against those requirements. The framework is assessed for its potential applicability and usefulness through a qualitative study with HRI experts. The framework is appreciated as a systematic tool that enables documentation and discussion, and identified issues inform the framework’s iteration. The themes of ownership of this process in interdisciplinary teams and its role in iteratively designing interfaces are discussed.

Several studies have been reported on the use of social robots for dementia care. These robots have been used for diverse tasks such as for companionship, as an exercise coach, and as daily life assistant. However, most of these studies have assessed impact on participants only at the time when the interaction takes place rather than their medium or long-term effects. In this work, we report on a nine-week study conducted in a nursing home in which a autonomous social robot, called Eva, acts as facilitator of a cognitive stimulation therapy (CST). During the study, eight persons with dementia interacted with the robot in a group session which included elements of music therapy, reminiscence, cognitive games, and relaxation. Using the Neuropsychiatric Inventory - Nursing Home version (NPI-NH), we analyzed the impact of the therapy guided by the robot. The results show a statistically significant decrease in the total score of NPI-NH. Also, three dementia-related symptoms: delusions, agitation/aggression, and euphoria/exaltation, show a statistically significant decrease after the intervention. In addition, a qualitative analysis on interviews conducted with caregivers shows that all participants exhibits positive short-term effects after the session and provides insights on why some changes in behavior prevailed beyond the therapy sessions. Our results provide evidence that a social robot could play a role in improving the quality of life of persons with dementia.

Many new and advanced technologies have been designed to develop the use of CGI and animation in the film-making industry. However, very little research exists on the possibility of using robotic technologies in stop-motion animation. This study examines the crucial effect of using robotic techniques in stop-motion animation Besides, it aims to develop robotic technologies that serve to reduce the cost, time, and risks of creating animation with the use of stop-motion art. We collect observations from expert's interviews to provide preliminary evidence of the possibilities of the human robot interaction in stops-motion. As media artists and researchers, we are looking for possible solutions to preserve stop-motion art. In a similar vein, this study will further explore the effectiveness of robotic technologies in different film-making dimensions according to the opinions of expert animators.

The uncanny valley phenomenon has be researched for the past 15 years, attempting to prove its validity with limited success. Researchers have been trying to recreate Masahiro Mori's hypothesised function [1] through a variety of experiments using images of real robots and images created from morphing humans and robots. Although some of these experiments have provided results supporting Mori's hypothesis, there is no solid confirmation of their legitimacy when it comes to real human-robot interaction. This paper examines the methods and subsequent results of studies to draw conclusions regarding the validity of experimental data into Mori's hypothesis. These conclusions lead us to propose an Augmented Reality(AR) experiment designed to verify the results of previous experimentation.

This research proposal aims to attract interest towards a future problem in the design of social robots. Persuasive technologies have demonstrated their effectiveness to negatively impact a user's behavior and generate addictions towards current social technologies. It could be the case that in the future similar persuasive technologies will be implemented in the design of social robots, leading to a recurrence of the negative impacts mentioned above. This research aims to investigate this phenomenon to prevent it and encourage the ethical design of social robots.

In this study, we set out to ask three questions. First, does lexical entrainment with a robot interlocutor persist after an interaction? Second, how does the influence of social robots on humans compare with the influence of humans on each other? Finally, what role is played by personality traits in lexical entrainment to robots, and how does this compare with the role of personality in entrainment to other humans? Our experiment shows that first, robots can indeed prompt lexical entrainment that persists after an interaction is over. This finding is interesting since it demonstrates that speakers can be linguistically influenced by a robot, in a way that is not merely motivated by a desire to be understood. Second, we find similarities between lexical entrainment to the robot peer and lexical entrainment to a human peer, although the effects are stronger when the peer is human. Third, we find that whether the peer is a robot or a human, similar personality traits contribute to lexical entrainment. In both peer conditions, participants who score higher on ``Openness to experience" are more likely to adopt less conventional terminology.

In this paper we described how robots can be used as cultural preservationists. As part of larger a case study, Nao Robots are used programmed to preserve New Zealand's most traditional war dance Haka.

Reciprocity is a cornerstone of human relationships and apparently it also appears in human-robot interaction independently of the context. It is expected that reciprocity will play a principal role in HRI in the future. The negative side of reciprocal phenomena has not been entirely explored in human-robot interaction. For instance, a reciprocal act such as bribery between Humans and robots is a very novel area. In this paper, we try to evaluate the questions: Can a robot bribe a human? To what extent is a robot bribing a human affect his/her reciprocal response? We performed an experiment using the Rock, Paper, Scissors game (RPSG). The robot bribes the participant by losing intentionally in certain rounds to obtain his/her favour later, and through using direct and indirect speech in certain rounds. The participants could obtain between 20%- 25% more money when the robot bribed them than in the control condition. The robot also used either direct or indirect speech requesting a favour in a second task. Our results show that the bribing robot received significantly less reciprocation than in the control condition regardless of whether the request was couched in direct or indirect speech. However there is a significant interaction effect between the bribe and speech conditions. Moreover, just three of sixty participants reported the robot-bribe in an interview as a malfunction, though they did not mention any moral judgement about its behaviour. Further, just 10% of the participants reported the bribe in the online questionnaire. We consider that our experiment makes an early contribution to continue the exploration of morally ambiguous and controversial reciprocal situations in HRI. Robot designers should consider the reciprocal human response towards robots in different contexts including bribery scenarios. Additionally our study could be used in guidelines for robot behavioural design to model future HRI interactions in terms of moral decisions.

In this paper we introduce the use of the neologism “Robotology” as the study of the impact of robotics on human reality. We offer an example of a first case study for future Robotologists analysing the meta-phenomena related with a humanoid robot performing a Maori Kapa Haka.

In this paper we described how robots can be used as cultural preservationists. As part of larger a case study, Nao Robots are programmed to preserve New Zealand's most traditional war dance, the Haka.

In this paper we present a study that investigates human-robot interpersonal distances and the influence of posture, either sitting or standing on the interpersonal distances. The study is based on a human approaching a robot and a robot approaching a human, in which the human/robot maintain either a sitting or standing posture while being approached. We collected and analysed data from twenty-two participants and the results revealed that robot posture has a significant impact on the interpersonal distances in human-robot interactions. Previous interactions with a robot, and lower negative attitudes towards robots also impacted interpersonal distances. Although the effects of gender, height and age did not yield significant results, we discuss their influence on the interpersonal distances between humans and robots and how they are of interest for future research. We present design implications for human-robot interaction research and humanoid robot design.

In this paper the nexus between fictional and real robots in Human Robot Interaction (HRI) is explored. We claim that design guidelines for HRI must not be borrowed from fiction blindly, as contradictions between the two may emerge with respect to what is desired by the human user. To understand human perception of robots appearing in movies we analyse viewing statistics and qualitative comments of a set of YouTube videos comprising of fictional and real robots. Analysis of the viewing statistics showed that real robots are more popular. Furthermore, analysis of the comments showed that two real robots (Nao and Shakey) generated significantly more positive comments and significantly more attributions of usage in human society as compared to the two fictional robots (AstroBoy and HAL9000). Based on the sample of robots considered in this research, our results reveal that contrary to expectation humans are more exposed to real robots and are more preferred, and we conclude by reasserting the contradiction that emerges between real and fictional robots.

HRI is a multi-disciplinary research field and integrating the range of expertise into a single project can be challenging. Enabling experts on human behavior to design fluent animations and behaviors for advanced robots is problematic, since the tools available for such robots are often in their prototype stage. We have built The Robot Engine (TRE) based on the Unity 3D Game Engine to control robots with Unity 3D. Unity 3D allows non-programmers to use a set of powerful animation and interaction design tools to visually program and animate robots. We review several animation techniques that are common in computer games and that could make the movements of robots more natural and convincing. We demonstrate the use of TRE with two different Arduino based robot platforms and believe that it can easily be extended for use with other robots. We further believe that this unconventional integration of technologies has the potential to fully bring the expertise of interaction designers into the process of advanced human-robot interaction projects.

We observe that in comparison to other media; online videos are a powerful outreach instrument for Computing technologies in general and specifically for Human Robot Interaction (HRI). Our experience in creating demos, scenarios and educational videos (we present two anecdotal accounts of educational HRI videos) leads us to believe that such modalities can have large pedagogical value if they are designed and conveyed appropriately. We also found that the rapid hand drawing (exemplified through fast frames in video) is an interesting technique to propagate science facts around social media. We also discuss the prospects of utilising standard MOOC's as a medium to teach HRI and thereby present a proposal of a (nano-MOOC). Furthermore, we also discuss the main challenge of being able to promote retention through HRI pedagogical material as there may be a tendency of withdrawal to deeply technical content. In conclusion, we present guidelines to promote the uptake of HRI across not only educational institutes but also the general public.

The question put forward in this paper is whether robots can create conformity by means of group pressure. We recreate and expand on a classic social psychology experiment by Solomon Asch, so as to explore three main dimensions. First, we wanted to know whether robots can prompt conformity in human subjects, and whether there is a significant difference between the degree to which individuals conform to a group of robots as opposed to a group of humans. Secondly we ask whether group pressure (from human or robot peers) can exert influence in verbal judgments, analogously to the influence on visual judgments that is known from previous research [3], [2]. Thirdly, we investigate whether the level of conformity differs between an ambiguous situation and a non-ambiguous situation. Our results show that in both visual and verbal tasks, participants exhibit conformity with human peers, but not with robot peers. The social influence of robot peers is not a significant predictor of verbal or visual judgments in our tasks. Furthermore, the level of conformity is significantly higher in an ambiguous (unclear) situation.

This paper seeks to establish a precedent for future development and design of social robots by considering the knowledge and expectations about robots of a group of 296 children. Human-robot interaction experiments were conducted with a Tele-operated anthropomorphic robot, and surveys were taken before and after the experiments. Children were also asked to perform a drawing of a robot. An image analysis algorithm was developed to classify drawings into 4 types: Anthropomorphic Mechanic/Non Mechanic (AM/AnM) and Non-Anthropomorphic Mechanic/Non Mechanic (nAM/nAnM). Image analysis algorithm was used in combination with human classification using a 2oo3 (two out of three) voting scheme to find children's strongest stereotype about robots. Survey and image analysis results suggest that children in general have some general knowledge about robots, and some children even have a deep understanding and expectations for future robots. Moreover, children's strongest stereotype is directed towards mechanical anthropomorphic systems.

This paper seeks to describe the knowledge and perceptions of robots that have a group of 19 children under 11 years old in Mexico City has, interacting with a teleoperated anthropomorphic robot compared with a group of 18 children with the same characteristics that have not interacted with same robot. We seek to focus a precedent in the Mexican children in their context for future development of social robots.