arXiv subjects

Hatice Kose

Publications and source records attributed to Hatice Kose.

3 recordsLinked to original sources

arXiv2024

Social Robot Navigation with Adaptive Proxemics Based on Emotions

The primary aim of this paper is to investigate the integration of emotions into the social navigation framework to analyse its effect on both navigation and human physiological safety and comfort. The proposed framework uses leg detection to find the whereabouts of people and computes adaptive proxemic zones based on their emotional state. We designed several case studies in a simulated environment and examined 3 different emotions; positive (happy), neutral and negative (angry). A survey study was conducted with 70 participants to explore their impressions about the navigation of the robot and compare the human safety and comfort measurements results. Both survey and simulation results showed that integrating emotions into proxemic zones has a significant effect on the physical safety of a human. The results revealed that when a person is angry, the robot is expected to navigate further than the standard distance to support his/her physiological comfort and safety. The results also showed that reducing the navigation distance is not preferred when a person is happy.

cs.RO↗

arXiv2023

Geometric Reinforcement Learning For Robotic Manipulation

Reinforcement learning (RL) is a popular technique that allows an agent to learn by trial and error while interacting with a dynamic environment. The traditional Reinforcement Learning (RL) approach has been successful in learning and predicting Euclidean robotic manipulation skills such as positions, velocities, and forces. However, in robotics, it is common to encounter non-Euclidean data such as orientation or stiffness, and failing to account for their geometric nature can negatively impact learning accuracy and performance. In this paper, to address this challenge, we propose a novel framework for RL that leverages Riemannian geometry, which we call Geometric Reinforcement Learning (G-RL), to enable agents to learn robotic manipulation skills with non-Euclidean data. Specifically, G-RL utilizes the tangent space in two ways: a tangent space for parameterization and a local tangent space for mapping to a nonEuclidean manifold. The policy is learned in the parameterization tangent space, which remains constant throughout the training. The policy is then transferred to the local tangent space via parallel transport and projected onto the non-Euclidean manifold. The local tangent space changes over time to remain within the neighborhood of the current manifold point, reducing the approximation error. Therefore, by introducing a geometrically grounded pre- and post-processing step into the traditional RL pipeline, our G-RL framework enables several model-free algorithms designed for Euclidean space to learn from non-Euclidean data without modifications. Experimental results, obtained both in simulation and on a real robot, support our hypothesis that G-RL is more accurate and converges to a better solution than approximating non-Euclidean data.

cs.RO↗

arXiv2021

Child-Robot Interaction Studies During COVID-19 Pandemic

The coronavirus disease (COVID-19) pandemic affected our lives deeply, just like everyone else, the children also suffered from the restrictions due to COVID-19 affecting their education and social interactions with others, being restricted from play areas and schools for a long time. Although social robots provide a promising solution to support children in their education, healthcare and social interaction with others, the precautions due to COVID-19 also introduced new constraints in the social robotics research. In this paper, we will discuss the benefits and challenges encountered in child-robot interaction due to COVID-19 based on two user studies. The first study involves children with hearing disabilities, and Pepper humanoid robot to support their audiometry tests. The second study includes the child-sized humanoid robot Kaspar and interaction games with children with autism spectrum disorder (ASD).

cs.HC↗