Dražen Brščić
Biographic Data
| ID | 5145709 |
|---|---|
| NAME | Dražen Brščić |
| GIVEN NAMES | Dražen |
| FAMILY NAME | Brščić |
| SIGNATURE | BRŠČIĆ D |
| AFFILIATIONS | Kyoto University |
| ORCID | 0000-0001-8477-6460 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 8 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
To Help or Not to Help
Robots are often designed to help, but help is not always helpful. In everyday situations, it is a socially delicate act: the right offer of help at the wrong moment can be intrusive, unnecessary, or even undermining. In this article, we challenge the prevailing assumption that robots should always offer help, prompting an essential discussion of how robots can discern when to offer help. We introduce a theoretical framework that enables robots t…
How Can Robots Acquire Venue-Specific Social Strategies? The Case of Robot Clerks Learning Sales Negotiations
Social robots are gradually being integrated into diverse social spheres, assuming multifaceted roles such as store clerks. Many of the interactive tasks entrusted to these robots are intricate, requiring more than simply responding to requests, and thus are not easily preprogrammed. One such task is sales negotiation. Empowering robots with negotiation skills is challenging due to the nuanced, context-dependent nature of effective tactics, which…
Investigation of Low-Moral Actions by Malicious Anonymous Operators of Avatar Robots
Avatar robots allow a teleoperator to interact with the people and environment of a remote place. Malicious operators can use this technology to perpetrate malicious or low-moral actions. In this study, we used hazard identification workshops to identify low-moral actions that are possible through the locomotor movement, cameras, and microphones of an avatar robot. We conducted three workshops, each with four potential future users of avatars, to…
Expressing Anger with Robot for Tackling the Onset of Robot Abuse
Service robots in public spaces can sometimes become victims of ‘abuse’, manifesting as persistent blocking of the robot’s path, physical violence such as pushing or pulling, or abusive language. This kind of abuse can be a serious obstacle to the deployment of robots. We studied the possibility of mitigating obstructive behaviour towards the robot, which is known to happen in the early stages of robot abuse, by having the robot express anger-lik…
Simulation-Based Machine Learning for Detection of Rarely Occurring Behaviors Based on Human Trajectories
This study focuses on the identification of rarely occurring human behaviors that could pose a potential security threat in public spaces, especially relevant to autonomous robotic bodyguards. The goal is to develop machine learning models capable of recognizing behaviors indicative of hostile intent. Rarely occurring behaviors in this context refer to unusual movement patterns, such as loitering with suspicious intent, sudden directional changes…
How Should a Robot do Indirect Handovers Politely
In this work, we studied the action of a robot handing over an object to a human. We focused on indirect handovers, where the robot does not pass the object directly into the human’s hand, but instead puts it down somewhere in the person’s vicinity where they can easily pick it up. In particular, we were interested in understanding what makes an indirect handover perceived as polite. Based on an in-lab collection of data from experienced waiters …
Robots as moral environments
Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information
Data-driven imitation learning enables service robots to learn social interaction behaviors, but these systems cannot adapt after training to changes in the environment, such as changing products in a store. To solve this, a novel learning system that uses neural attention and approximate string matching to copy information from a product information database to its output is proposed. A camera shop interaction dataset was simulated for training/…
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Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information
Data-driven imitation learning enables service robots to learn social interaction behaviors, but these systems cannot adapt after training to changes in the environment, such as changing products in a store. To solve this, a novel learning system that uses neural attention and approximate string matching to copy information from a product information database to its output is proposed. A camera shop interaction dataset was simulated for training/…
Robots as moral environments
Investigation of Low-Moral Actions by Malicious Anonymous Operators of Avatar Robots
Avatar robots allow a teleoperator to interact with the people and environment of a remote place. Malicious operators can use this technology to perpetrate malicious or low-moral actions. In this study, we used hazard identification workshops to identify low-moral actions that are possible through the locomotor movement, cameras, and microphones of an avatar robot. We conducted three workshops, each with four potential future users of avatars, to…
Expressing Anger with Robot for Tackling the Onset of Robot Abuse
Service robots in public spaces can sometimes become victims of ‘abuse’, manifesting as persistent blocking of the robot’s path, physical violence such as pushing or pulling, or abusive language. This kind of abuse can be a serious obstacle to the deployment of robots. We studied the possibility of mitigating obstructive behaviour towards the robot, which is known to happen in the early stages of robot abuse, by having the robot express anger-lik…
Simulation-Based Machine Learning for Detection of Rarely Occurring Behaviors Based on Human Trajectories
This study focuses on the identification of rarely occurring human behaviors that could pose a potential security threat in public spaces, especially relevant to autonomous robotic bodyguards. The goal is to develop machine learning models capable of recognizing behaviors indicative of hostile intent. Rarely occurring behaviors in this context refer to unusual movement patterns, such as loitering with suspicious intent, sudden directional changes…
How Should a Robot do Indirect Handovers Politely
In this work, we studied the action of a robot handing over an object to a human. We focused on indirect handovers, where the robot does not pass the object directly into the human’s hand, but instead puts it down somewhere in the person’s vicinity where they can easily pick it up. In particular, we were interested in understanding what makes an indirect handover perceived as polite. Based on an in-lab collection of data from experienced waiters …
To Help or Not to Help
Robots are often designed to help, but help is not always helpful. In everyday situations, it is a socially delicate act: the right offer of help at the wrong moment can be intrusive, unnecessary, or even undermining. In this article, we challenge the prevailing assumption that robots should always offer help, prompting an essential discussion of how robots can discern when to offer help. We introduce a theoretical framework that enables robots t…
How Can Robots Acquire Venue-Specific Social Strategies? The Case of Robot Clerks Learning Sales Negotiations
Social robots are gradually being integrated into diverse social spheres, assuming multifaceted roles such as store clerks. Many of the interactive tasks entrusted to these robots are intricate, requiring more than simply responding to requests, and thus are not easily preprogrammed. One such task is sales negotiation. Empowering robots with negotiation skills is challenging due to the nuanced, context-dependent nature of effective tactics, which…
Robot (8 works) · Artificial Intelligence (6 works) · Computer Science (6 works) · Social Robot Interaction and HRI (5 works) · Human–computer interaction (4 works) · Psychology (4 works) · AI in Service Interactions (3 works) · Reinforcement Learning in Robotics (3 works) · Ethics and Social Impacts of AI (2 works) · Machine learning (2 works)