arXiv subjects

Youngjae Yoo

Publications and source records attributed to Youngjae Yoo.

4 recordsLinked to original sources

arXiv2026

LeagueBot: A Voice LLM Companion of Cognitive and Emotional Support for Novice Players in Competitive Games

Competitive games pose steep learning curves and strong social pressures, often discouraging novice players and limiting sustained engagement. To address these challenges, this study introduces LeagueBot, a large language model-based voice chatbot designed to provide both informational and emotional support during live gameplay in league of legends, one of the most competitive multiplayer online battle arena games. In a within-subjects experiment with 33 novice players, LeagueBot was found to reduce cognitive challenge, performative challenge, and perceived tension. Qualitative analysis further identified three themes: enhanced access to game information, relief from cognitive burden, and practical limitations. Participants noted that LeagueBot offered context-appropriate guidance and emotional support, helping ease the steep learning curve and psychological pressures of competitive gaming. Together, these findings underscore the potential of voice-based LLM companions to assist novice players in competitive environments and highlight their broader applicability for real-time support in other high-pressure contexts.

cs.HC↗

arXiv2025

Investigating the Integrated Digital Interventions Delivered by a Therapeutic Companion Agent for Young Adults with Symptoms of Depression: A Proof-of-Concept Study

Background: Despite the clinical effectiveness of digital interventions for young adults with depression, low engagement and adherence remain persistent challenges. Building a strong digital therapeutic alliance has been proposed to address these barriers. This study highlights the need for a conversational therapeutic companion agent (TCA)-based intervention design. Objective: This study aimed to develop a Wizard-of-Oz TCA-centered prototype integrating social-support-based ecological momentary assessment (EMA), ecological momentary intervention (EMI), behavioral activation, and gamification. We evaluated the six-week proof-of-concept efficacy of this intervention among young adults with depressive symptoms. Methods: Korean young adults aged 20--39 years with mild-to-moderate depressive symptoms (PHQ-9) were recruited online. The intervention group ($n = 29$) received a six-week TCA-based digital intervention, while the control group ($n = 29$), recruited four weeks later, continued their usual routines. The TCA guided four daily behavioral-activation tasks, three mood assessments, meditation, daily summaries, and weekly mission feedback. Both groups were assessed at baseline and at weeks 2, 4, and 6 using the BDI-II, GAD-7, and Q-LES-Q-SF. Results: Of 58 participants, 57 completed the study (one dropout in the intervention group). At week 6, the intervention group showed significantly greater reductions in depressive symptoms and improvements in quality of life than controls. Adherence was 78\% for EMA, 51\% for EMI, and 65\% for daily routines. Conclusions: The TCA-based digital intervention improved depressive symptoms and quality of life with adherence levels comparable to previous digital health interventions. Future studies should refine the TCA design and conduct larger-scale evaluations.

cs.HC↗

arXiv2024

Multi-Object RANSAC: Efficient Plane Clustering Method in a Clutter

In this paper, we propose a novel method for plane clustering specialized in cluttered scenes using an RGB-D camera and validate its effectiveness through robot grasping experiments. Unlike existing methods, which focus on large-scale indoor structures, our approach -- Multi-Object RANSAC emphasizes cluttered environments that contain a wide range of objects with different scales. It enhances plane segmentation by generating subplanes in Deep Plane Clustering (DPC) module, which are then merged with the final planes by post-processing. DPC rearranges the point cloud by voting layers to make subplane clusters, trained in a self-supervised manner using pseudo-labels generated from RANSAC. Multi-Object RANSAC demonstrates superior plane instance segmentation performances over other recent RANSAC applications. We conducted an experiment on robot suction-based grasping, comparing our method with vision-based grasping network and RANSAC applications. The results from this real-world scenario showed its remarkable performance surpassing the baseline methods, highlighting its potential for advanced scene understanding and manipulation.

cs.RO↗

arXiv2024

Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots

Object slip perception is essential for mobile manipulation robots to perform manipulation tasks reliably in the dynamic real-world. Traditional approaches to robot arms' slip perception use tactile or vision sensors. However, mobile robots still have to deal with noise in their sensor signals caused by the robot's movement in a changing environment. To solve this problem, we present an anomaly detection method that utilizes multisensory data based on a deep autoencoder model. The proposed framework integrates heterogeneous data streams collected from various robot sensors, including RGB and depth cameras, a microphone, and a force-torque sensor. The integrated data is used to train a deep autoencoder to construct latent representations of the multisensory data that indicate the normal status. Anomalies can then be identified by error scores measured by the difference between the trained encoder's latent values and the latent values of reconstructed input data. In order to evaluate the proposed framework, we conducted an experiment that mimics an object slip by a mobile service robot operating in a real-world environment with diverse household objects and different moving patterns. The experimental results verified that the proposed framework reliably detects anomalies in object slip situations despite various object types and robot behaviors, and visual and auditory noise in the environment.

cs.RO↗