Large language models enable unscripted conversations while maintaining a consistent personality. One desirable personality trait in cooperative partners, known to improve task performance, is agreeableness. To explore the impact of large language models on personality modeling for robots, as well as the effect of agreeable and non-agreeable personalities in cooperative tasks, we conduct a two-part study. This includes an online pre-study for personality validation and a lab-based main study to evaluate the effects on likability, motivation, and task performance. The results demonstrate that the robot’s agreeableness significantly enhances its likability. No significant difference in intrinsic motivation was observed between the two personality types. However, the findings suggest that a robot exhibiting agreeableness and openness to new experiences can enhance task performance. This study highlights the advantages of employing large language models for customized modeling of robot personalities and provides evidence that a carefully chosen agreeable robot personality can positively influence human perceptions and lead to greater success in cooperative scenarios.

With the increasing performance of text-to-speech systems and their generated voices indistinguishable from natural human speech, the use of these systems for robots raises ethical and safety concerns. A robot with a natural voice could increase trust, which might result in over-reliance despite evidence for robot unreliability. To estimate the influence of a robot’s voice on trust and compliance, we design a study that consists of two experiments. In a pre-study ( \(N_{1}=60\) ) the most suitable natural and mechanical voice for the main study are estimated and selected for the main study. Afterward, in the main study ( \(N_{2}=68\) ), the influence of a robot’s voice on trust and compliance is evaluated in a cooperative game of Battleship with a robot as an assistant. During the experiment, the acceptance of the robot’s advice and response time are measured, which indicate trust and compliance, respectively. The results show that participants expect robots to sound human-like and that a robot with a natural voice is perceived as safer. Additionally, a natural voice can affect compliance. Despite repeated incorrect advice, the participants are more likely to rely on the robot with the natural voice. The results do not show a direct effect on trust. Natural voices provide increased intelligibility, and while they can increase compliance with the robot, the results indicate that natural voices might not lead to over-reliance. The results highlight the importance of incorporating voices into the design of social robots to improve communication, avoid adverse effects, and increase acceptance and adoption in society.

Abstract The paper introduces CycleIK, a neuro-robotic approach that wraps two novel neuro-inspired methods for the inverse kinematics (IK) task—a Generative Adversarial Network (GAN), and a Multi-Layer Perceptron architecture. These methods can be used in a standalone fashion, but we also show how embedding these into a hybrid neuro-genetic IK pipeline allows for further optimization via sequential least-squares programming (SLSQP) or a genetic algorithm (GA). The models are trained and tested on dense datasets that were collected from random robot configurations of the new Neuro-Inspired COLlaborator (NICOL), a semi-humanoid robot with two redundant 8-DoF manipulators. We utilize the weighted multi-objective function from the state-of-the-art BioIK method to support the training process and our hybrid neuro-genetic architecture. We show that the neural models can compete with state-of-the-art IK approaches, which allows for deployment directly to robotic hardware. Additionally, it is shown that the incorporation of the genetic algorithm improves the precision while simultaneously reducing the overall runtime.

Increasing anthropomorphic robot behavioral design could affect trust and cooperation positively. However, studies have shown contradicting results and suggest a task-dependent relationship between robots that display emotions and trust. Therefore, this study analyzes the effect of robots that display human-like emotions on trust, cooperation, and participants’ emotions. In the between-group study, participants play the coin entrustment game with an emotional and a non-emotional robot. The results showthat the robot that displays emotions induces more anxiety than the neutral robot. Accordingly, the participants trust the emotional robot less and are less likely to cooperate. Furthermore, the perceived intelligence of a robot increases trust, while a desire to outcompete the robot can reduce trust and cooperation. Thus, the design of robots expressing emotions should be task dependent to avoid adverse effects that reduce trust and cooperation.

Recent developments in explainable artificial intelligence promise the potential to transform human-robot interaction: Explanations of robot decisions could affect user perceptions, justify their reliability, and increase trust. However, the effects on human perceptions of robots that explain their decisions have not been studied thoroughly. To analyze the effect of explainable robots, we conduct a study in which two simulated robots play a competitive board game. While one robot explains its moves, the other robot only announces them. Providing explanations for its actions was not sufficient to change the perceived competence, intelligence, likeability or safety ratings of the robot. However, the results show that the robot that explains its moves is perceived as more lively and human-like. This study demonstrates the need for and potential of explainable human-robot interaction and the wider assessment of its effects as a novel research direction.

As robots become more advanced and capable, developing trust is an important factor of human-robot interaction and cooperation. However, as multiple environmental and social factors can influence trust, it is important to develop more elaborate scenarios and methods to measure human-robot trust. A widely used measurement of trust in social science is the investment game. In this study, we propose a scaled-up, immersive, science fiction Human-Robot Interaction (HRI) scenario for intrinsic motivation on human-robot collaboration, built upon the investment game and aimed at adapting the investment game for human-robot trust. For this purpose, we utilise two Neuro-Inspired Companion (NICO) - robots and a projected scenery. We investigate the applicability of our space mission experiment design to measure trust and the impact of non-verbal communication. We observe a correlation of 0.43 (p=0.02)between self-assessed trust and trust measured from the game and a positive impact of non-verbal communication on trust (p=0.0008) and robot perception for anthropomorphism (p=0.007) and animacy (p=0.00002). We conclude that our scenario is an appropriate method to measure trust in human-robot interaction and also to study how non-verbal communication influences a human's trust in robots.

In this paper, we present an autonomous AI system designed for a Human-Robot Interaction (HRI) study, set around a dice game scenario. We conduct a case study to answer our research question: Does a robot with a socially engaged personality lead to a higher acceptance than a competitive personality? The flexibility of our proposed system allows us to construct and attribute two different personalities to a humanoid robot: a socially engaged personality that maximizes its user interaction and a competitive personality that is focused on playing and winning the game. We evaluate both personalities in a user study, in which the participants play a turn-taking dice game with the robot. Each personality is assessed with four different evaluation tools: 1) the Godspeed Questionnaire, 2) the Mind Perception Questionnaire, 3) a custom questionnaire concerning the overall HRI experience, and 4) a Convolutional Neural Network analyzing the emotions on the participants' facial feedback throughout the game. Our results show that the socially engaged personality evokes stronger emotions among the participants and is rated higher in likability and animacy than the competitive one. We conclude that designing the robot with a socially engaged personality contributes to a higher acceptance within an HRI scenario.

We present an embodied neural model for haptic object classification by active haptic exploration with the humanoid robot NICO. When NICO's newly developed robotic hand closes around an object, multiple sensory readings from a tactile fingertip sensor, motor positions, and motor currents are recorded. We created a haptic dataset with 83200 haptic measurements, based on 100 samples of each of 16 different objects, every sample containing 52 measurements. First, we provide an analysis of neural classification models with regard to isolated haptic sensory channels for object classification. Based on this, we develop a series of neural models (MLP, CNN, LSTM) that integrate the haptic sensory channels to classify explored objects. As an initial baseline, our best model achieves a 66.6% classification accuracy over 16 objects. We show that this result is due to the ability of the network to integrate the haptic data both over time domain and over different haptic sensory channels. Furthermore, we make the dataset publically available to address the issue of sparse haptic datasets for machine learning research.

Human-Robot Interaction (HRI) studies, particularly the ones designed around social robots, use emotions as important building blocks for interaction design. In order to provide a natural interaction experience, these social robots need to recognise the emotions expressed by the users across various modalities of communication and use them to estimate an internal affective model of the interaction. These internal emotions act as motivation for learning to respond to the user in different situations, using the physical capabilities of the robot. This paper proposes a deep hybrid neural model for multi-modal affect recognition, analysis and behaviour modelling in social robots. The model uses growing self-organising network models to encode intrinsic affective states for the robot. These intrinsic states are used to train a reinforcement learning model to learn facial expression representations on the Neuro-Inspired Companion (NICO) robot, enabling the robot to express empathy towards the users.

We present a novel approach for interactive auditory object analysis with a humanoid robot. The robot elicits sensory information by physically shaking visually indistinguishable plastic capsules. It gathers the resulting audio signals from microphones that are embedded into the robotic ears. A neural network architecture learns from these signals to analyze properties of the contents of the containers. Specifically, we evaluate the material classification and weight prediction accuracy and demonstrate that the framework is fairly robust to acoustic real-world noise.

We present an illustrative example of an interactive auditory perception approach performed by a humanoid robot called NICO, the Neuro Inspired COmpanion [1]. The video demonstrates a material classification task in the style of a classic TV game show. NICO and another candidate are supposed to determine the content of small plastic capsules that are visually indistinguishable. Shaking the capsules produces audio signals that range from rattling stones, over tinkling coins to swooshing sand. NICO can perceive and analyze these sounds to determine the material of the capsules content.

Advancements in Human-Robot Interaction involve robots being more responsive and adaptive to the human user they are interacting with. For example, robots model a personalised dialogue with humans, adapting the conversation to accommodate the user's preferences in order to allow natural interactions. This study investigates the impact of such personalised interaction capabilities of a human companion robot on its social acceptance, perceived intelligence and likeability in a human-robot interaction scenario. In order to measure this impact, the study makes use of an object learning scenario where the user teaches different objects to the robot using natural language. An interaction module is built on top of the learning scenario which engages the user in a personalised conversation before teaching the robot to recognise different objects. The two systems, i.e. with and without the interaction module, are compared with respect to how different users rate the robot on its intelligence and sociability. Although the system equipped with personalised interaction capabilities is rated lower on social acceptance, it is perceived as more intelligent and likeable by the users.

Human companion robots need to be sociable and responsive towards emotions to better interact with the human environment they are expected to operate in. This paper is based on the Neuro-Inspired COmpanion robot (NICO) and investigates a hybrid, deep neural network model to teach the NICO to associate perceived emotions with expression representations using its on-board capabilities. The proposed model consists of a Convolutional Neural Network (CNN) and a Self-organising Map (SOM) to perceive the emotions expressed by a human user towards NICO and trains two parallel Multilayer Perceptron (MLP) networks to learn general as well as person-specific associations between perceived emotions and the robot's facial expressions.

Interdisciplinary research, drawing from robotics, artificial intelligence, neuroscience, psychology, and cognitive science, is a cornerstone to advance the state-of-the-art in multimodal human-robot interaction and neuro-cognitive modeling. Research on neuro-cognitive models benefits from the embodiment of these models into physical, humanoid agents that possess complex, human-like sensorimotor capabilities for multimodal interaction with the real world. For this purpose, we develop and introduce NICO (Neuro-Inspired COmpanion), a humanoid developmental robot that fills a gap between necessary sensing and interaction capabilities and flexible design. This combination makes it a novel neuro-cognitive research platform for embodied sensorimotor computational and cognitive models in the context of multimodal interaction as shown in our results.

This paper describes the techniques used in the submitted video presenting an interaction scenario, realised using the Neuro-Inspired Companion (NICO) robot. NICO engages the users in a personalised conversation where the robot always tracks the users' face, remembers them and interacts with them using natural language. NICO can also learn to perform tasks such as remembering and recalling objects and thus can assist users in their daily chores. The interaction system helps the users to interact as naturally as possible with the robot, enriching their experience with the robot, making it more interesting and engaging.

In this paper we present a multi-modal human robot interaction architecture which is able to combine information coming from different sensory inputs, and can generate feedback for the user which helps to teach him/her implicitly how to interact with the robot. The system combines vision, speech and language with inference and feedback. The system environment consists of a Nao robot which has to learn objects situated on a table only by understanding absolute and relative object locations uttered by the user and afterwards points on a desired object to show what it has learned. The results of a user study and performance test show the usefulness of the feedback produced by the system and also justify the usage of the system in a real-world applications, as its classification accuracy of multi-modal input is around 80.8%. In the experiments, the system was able to detect inconsistent input coming from different sensory modules in all cases and could generate useful feedback for the user from this information.