arXiv subjects

Fethiye Irmak Dogan

Publications and source records attributed to Fethiye Irmak Dogan.

25 records · Page 2Linked to original sources

arXiv2025

Robot-Led Vision Language Model Wellbeing Assessment of Children

This study presents a novel robot-led approach to assessing children's mental wellbeing using a Vision Language Model (VLM). Inspired by the Child Apperception Test (CAT), the social robot NAO presented children with pictorial stimuli to elicit their verbal narratives of the images, which were then evaluated by a VLM in accordance with CAT assessment guidelines. The VLM's assessments were systematically compared to those provided by a trained psychologist. The results reveal that while the VLM demonstrates moderate reliability in identifying cases with no wellbeing concerns, its ability to accurately classify assessments with clinical concern remains limited. Moreover, although the model's performance was generally consistent when prompted with varying demographic factors such as age and gender, a significantly higher false positive rate was observed for girls, indicating potential sensitivity to gender attribute. These findings highlight both the promise and the challenges of integrating VLMs into robot-led assessments of children's wellbeing.

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arXiv2025

BT-ACTION: A Test-Driven Approach for Modular Understanding of User Instruction Leveraging Behaviour Trees and LLMs

Natural language instructions are often abstract and complex, requiring robots to execute multiple subtasks even for seemingly simple queries. For example, when a user asks a robot to prepare avocado toast, the task involves several sequential steps. Moreover, such instructions can be ambiguous or infeasible for the robot or may exceed the robot's existing knowledge. While Large Language Models (LLMs) offer strong language reasoning capabilities to handle these challenges, effectively integrating them into robotic systems remains a key challenge. To address this, we propose BT-ACTION, a test-driven approach that combines the modular structure of Behavior Trees (BT) with LLMs to generate coherent sequences of robot actions for following complex user instructions, specifically in the context of preparing recipes in a kitchen-assistance setting. We evaluated BT-ACTION in a comprehensive user study with 45 participants, comparing its performance to direct LLM prompting. Results demonstrate that the modular design of BT-ACTION helped the robot make fewer mistakes and increased user trust, and participants showed a significant preference for the robot leveraging BT-ACTION. The code is publicly available at https://github.com/1Eggbert7/BT_LLM.

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arXiv2025

A Model-Agnostic Approach for Semantically Driven Disambiguation in Human-Robot Interaction

Ambiguities are inevitable in human-robot interaction, especially when a robot follows user instructions in a large, shared space. For example, if a user asks the robot to find an object in a home environment with underspecified instructions, the object could be in multiple locations depending on missing factors. For instance, a bowl might be in the kitchen cabinet or on the dining room table, depending on whether it is clean or dirty, full or empty, and the presence of other objects around it. Previous works on object search have assumed that the queried object is immediately visible to the robot or have predicted object locations using one-shot inferences, which are likely to fail for ambiguous or partially understood instructions. This paper focuses on these gaps and presents a novel model-agnostic approach leveraging semantically driven clarifications to enhance the robot's ability to locate queried objects in fewer attempts. Specifically, we leverage different knowledge embedding models, and when ambiguities arise, we propose an informative clarification method, which follows an iterative prediction process. The user experiment evaluation of our method shows that our approach is applicable to different custom semantic encoders as well as LLMs, and informative clarifications improve performances, enabling the robot to locate objects on its first attempts. The user experiment data is publicly available at https://github.com/IrmakDogan/ExpressionDataset.

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arXiv2025

Let's move on: Topic Change in Robot-Facilitated Group Discussions

Robot-moderated group discussions have the potential to facilitate engaging and productive interactions among human participants. Previous work on topic management in conversational agents has predominantly focused on human engagement and topic personalization, with the agent having an active role in the discussion. Also, studies have shown the usefulness of including robots in groups, yet further exploration is still needed for robots to learn when to change the topic while facilitating discussions. Accordingly, our work investigates the suitability of machine-learning models and audiovisual non-verbal features in predicting appropriate topic changes. We utilized interactions between a robot moderator and human participants, which we annotated and used for extracting acoustic and body language-related features. We provide a detailed analysis of the performance of machine learning approaches using sequential and non-sequential data with different sets of features. The results indicate promising performance in classifying inappropriate topic changes, outperforming rule-based approaches. Additionally, acoustic features exhibited comparable performance and robustness compared to the complete set of multimodal features. Our annotated data is publicly available at https://github.com/ghadj/topic-change-robot-discussions-data-2024.

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arXiv2025

Feature Aggregation with Latent Generative Replay for Federated Continual Learning of Socially Appropriate Robot Behaviours

It is critical for robots to explore Federated Learning (FL) settings where several robots, deployed in parallel, can learn independently while also sharing their learning with each other. This collaborative learning in real-world environments requires social robots to adapt dynamically to changing and unpredictable situations and varying task settings. Our work contributes to addressing these challenges by exploring a simulated living room environment where robots need to learn the social appropriateness of their actions. First, we propose Federated Root (FedRoot) averaging, a novel weight aggregation strategy which disentangles feature learning across clients from individual task-based learning. Second, to adapt to challenging environments, we extend FedRoot to Federated Latent Generative Replay (FedLGR), a novel Federated Continual Learning (FCL) strategy that uses FedRoot-based weight aggregation and embeds each client with a generator model for pseudo-rehearsal of learnt feature embeddings to mitigate forgetting in a resource-efficient manner. Our results show that FedRoot-based methods offer competitive performance while also resulting in a sizeable reduction in resource consumption (up to 86% for CPU usage and up to 72% for GPU usage). Additionally, our results demonstrate that FedRoot-based FCL methods outperform other methods while also offering an efficient solution (up to 84% CPU and 92% GPU usage reduction), with FedLGR providing the best results across evaluations.

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arXiv2021

Leveraging Explainability for Comprehending Referring Expressions in the Real World

For effective human-robot collaboration, it is crucial for robots to understand requests from users and ask reasonable follow-up questions when there are ambiguities. While comprehending the users' object descriptions in the requests, existing studies have focused on this challenge for limited object categories that can be detected or localized with existing object detection and localization modules. On the other hand, in the wild, it is impossible to limit the object categories that can be encountered during the interaction. To understand described objects and resolve ambiguities in the wild, for the first time, we suggest a method by leveraging explainability. Our method focuses on the active regions of a scene to find the described objects without putting the previous constraints on object categories and natural language instructions. We evaluate our method in varied real-world images and observe that the regions suggested by our method can help resolve ambiguities. When we compare our method with a state-of-the-art baseline, we show that our method performs better in scenes with ambiguous objects which cannot be recognized by existing object detectors.

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arXiv2021

Using Depth for Improving Referring Expression Comprehension in Real-World Environments

In a human-robot collaborative task where a robot helps its partner by finding described objects, the depth dimension plays a critical role in successful task completion. Existing studies have mostly focused on comprehending the object descriptions using RGB images. However, 3-dimensional space perception that includes depth information is fundamental in real-world environments. In this work, we propose a method to identify the described objects considering depth dimension data. Using depth features significantly improves performance in scenes where depth data is critical to disambiguate the objects and across our whole evaluation dataset that contains objects that can be specified with and without the depth dimension.

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