Thomas Hellström
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View article: Personalized causal explanations of a robot’s behavior
Personalized causal explanations of a robot’s behavior Open
The deployment of robots in environments shared with humans implies that they must be able to justify or explain their behavior to nonexpert users when the user, or the situation itself, requires it. We propose a framework for robots to ge…
View article: Building a Self-Explanatory Social Robot on the Basis of an Explanation-Oriented Runtime Knowledge Model
Building a Self-Explanatory Social Robot on the Basis of an Explanation-Oriented Runtime Knowledge Model Open
In recent years, there has been growing interest in developing robots capable of explaining their behavior, thereby improving their acceptance by humans with whom they share their environment. Proposed software designs are typically based …
View article: Deep-learning based automated pancreas segmentation on CT scans of chronic pancreatitis patients
Deep-learning based automated pancreas segmentation on CT scans of chronic pancreatitis patients Open
The AI model demonstrated high accuracy and robustness in pancreas segmentation of both CP patients and healthy subjects, and across diverse sites and scanners, suggesting its potential for clinical application.
View article: Navigating the Human–Robot Interface—Exploring Human Interactions and Perceptions with Social and Telepresence Robots
Navigating the Human–Robot Interface—Exploring Human Interactions and Perceptions with Social and Telepresence Robots Open
This study investigates user experiences of interactions with two types of robots: Pepper, a social humanoid robot, and Double 3, a self-driving telepresence robot. Conducted in a controlled setting with a specific participant group, this …
View article: Navigating the Human-Robot Interface – Exploring Humans Interactions and Perceptions with Social and Telepresence Robots
Navigating the Human-Robot Interface – Exploring Humans Interactions and Perceptions with Social and Telepresence Robots Open
This study investigates user experiences of interaction with two types of robots: Pepper, a social humanoid robot, and Double 3, a self-driving telepresence robot. Conducted in a controlled setting with a specific participant group, this r…
View article: A Taxonomy of Embodiment in the AI Era
A Taxonomy of Embodiment in the AI Era Open
This paper presents a taxonomy of agents’ embodiment in physical and virtual environments. It categorizes embodiment based on five entities: the agent being embodied, the possible mediator of the embodiment, the environment in which sensin…
View article: Navigating the Human‐Robot Interface – Exploring Humans Interactions and Perceptions with Social and Telepresence Robots
Navigating the Human‐Robot Interface – Exploring Humans Interactions and Perceptions with Social and Telepresence Robots Open
This study explores Human-Robot Interaction (HRI) by investigating user experiences with two types of robots: Pepper, a social humanoid robot, and Double 3, a self-driving telepresence robot. Conducted in a controlled setting with a specif…
View article: The experience of humans' and robots' mutual (im)politeness in enacted service scenarios: An empirical study
The experience of humans' and robots' mutual (im)politeness in enacted service scenarios: An empirical study Open
The paper reports an empirical study of the effect of human treatment of a robot on the social perception of the robot's behavior. The study employed an enacted interaction between an anthropomorphic "waiter" robot and two customers. The r…
View article: AI and its consequences for the written word
AI and its consequences for the written word Open
The latest developments of chatbots driven by Large Language Models (LLMs), more specifically ChatGPT, have shaken the foundations of how text is created, and may drastically reduce and change the need, ability, and valuation of human writ…
View article: Policy regularization for legible behavior
Policy regularization for legible behavior Open
In this paper we propose a method to augment a Reinforcement Learning agent with legibility. This method is inspired by the literature in Explainable Planning and allows to regularize the agent’s policy after training, and without requirin…
View article: Policy Regularization for Legible Behavior
Policy Regularization for Legible Behavior Open
In Reinforcement Learning interpretability generally means to provide insight into the agent's mechanisms such that its decisions are understandable by an expert upon inspection. This definition, with the resulting methods from the literat…
View article: Natural language guided object retrieval in images
Natural language guided object retrieval in images Open
The ability to understand the surrounding environment and being able to communicate with interacting humans are important functionalities for many automated systems where visual input (e.g., images, video) and natural language input (speec…
View article: WoZ4U: An Open-Source Wizard-of-Oz Interface for Easy, Efficient and Robust HRI Experiments
WoZ4U: An Open-Source Wizard-of-Oz Interface for Easy, Efficient and Robust HRI Experiments Open
Wizard-of-Oz experiments play a vital role in Human-Robot Interaction (HRI), as they allow for quick and simple hypothesis testing. Still, a publicly available general tool to conduct such experiments is currently not available in the rese…
View article: Intent Recognition from Speech and Plan Recognition
Intent Recognition from Speech and Plan Recognition Open
In multi-agent systems, the ability to infer intentions allows artificial agents to act proactively and with partial information. In this paper we propose an algorithm to infer a speakers intentions with natural language analysis combined …
View article: Conversational Norms for Human-Robot Dialogues
Conversational Norms for Human-Robot Dialogues Open
This paper describes a recently initiated research project aiming at supporting development of computerised dialogue systems that handle breaches of conversational norms such as the Gricean maxims, which describe how dialogue participants …
View article: Traveling Drinksman: A Mobile Service Robot for People in Care-Homes
Traveling Drinksman: A Mobile Service Robot for People in Care-Homes Open
This paper describes ongoing work on the development of a service robot for serving drinks to people sitting at tables, for example in the recreation room of a care-house. The robot, denoted the Traveling Drinksman, should be able to detec…
View article: The relevance of causation in robotics: A review, categorization, and analysis
The relevance of causation in robotics: A review, categorization, and analysis Open
In this article, we investigate the role of causal reasoning in robotics research. Inspired by a categorization of human causal cognition, we propose a categorization of robot causal cognition. For each category, we identify related earlie…
View article: Verbal explanations by collaborating robot teams
Verbal explanations by collaborating robot teams Open
In this article, we present work on collaborating robot teams that use verbal explanations of their actions and intentions in order to be more understandable to the human. For this, we introduce a mechanism that determines what information…
View article: Bias in machine learning - what is it good for?
Bias in machine learning - what is it good for? Open
In public media as well as in scientific publications, the term bias is used in conjunction with machine learning in many different contexts, and with many different meanings. This paper proposes a taxonomy of these different meanings, ter…
View article: Bias in Machine Learning -- What is it Good for?
Bias in Machine Learning -- What is it Good for? Open
In public media as well as in scientific publications, the term \emph{bias} is used in conjunction with machine learning in many different contexts, and with many different meanings. This paper proposes a taxonomy of these different meanin…
View article: Bias in Machine Learning What is it Good (and Bad) for
Bias in Machine Learning What is it Good (and Bad) for Open
In public media as well as in scientific publications, the term \emph{bias} is used in conjunction with machine learning in many different contexts, and with many different meanings. This paper proposes a taxonomy of these different meanin…
View article: Development of a sweet pepper harvesting robot
Development of a sweet pepper harvesting robot Open
This paper presents the development, testing and validation of SWEEPER, a robot for harvesting sweet pepper fruit in greenhouses. The robotic system includes a six degrees of freedom industrial arm equipped with a specially designed end ef…
View article: Modelling Grice's Maxim of Quantity as Informativeness for Short Text
Modelling Grice's Maxim of Quantity as Informativeness for Short Text Open
Grice's Cooperative Principle (CP) is one of the early theories about good communication. We propose a novel formalisation of one of the sub-components of CP, namely the maxim of quantity (MoQ). We interpret MoQ as informativeness and assu…
View article: Unsupervised Inference of Object Affordance from Text Corpora
Unsupervised Inference of Object Affordance from Text Corpora Open
Affordances denote actions that can be performed in the presence of different objects, or possibility of action in an environment. In robotic systems, affordances and actions may suffer from poor semantic generalization capabilities due to…