--Improving human health and well-being requires an accurate and effective understanding of an individual's physical and mental state throughout daily life. T o support this goal, we utilized smartphones, smartwatches, and sleep sensors to collect data passively and continuously for 24 hours a day, with minimal interference to participants' usual behavior, enabling us to gather quantitative data on daily behaviors and sleep activities across multiple days. Additionally, we gathered subjective self-reports of participants' fatigue, stress, and sleep quality through surveys conducted immediately before and after sleep. This comprehensive lifelog dataset is expected to provide a foundational resource for exploring meaningful insights into human daily life and lifestyle patterns, and a portion of the data has been anonymized and made publicly available for further research. In this paper, we introduce the ETRI Lifelog Dataset 2024, detailing its structure and presenting potential applications, such as using machine learning models to predict sleep quality and stress. Human daily life consists of a complex interrelation of different activities and physiological states, spanning daytime behavior and nighttime sleep.

In standard reinforcement learning, an episode is defined as a sequence of interactions between agents and the environment, which terminates upon reaching a terminal state or a pre-defined episode length. Setting a shorter episode length enables the generation of multiple episodes with the same number of data samples, thereby facilitating an exploration of diverse states. While shorter episodes may limit the collection of long-term interactions, they may offer significant advantages when properly managed. For example, trajectory truncation in single-agent reinforcement learning has shown how the benefits of shorter episodes can be leveraged despite the trade-off of reduced long-term interaction experiences. However, this approach remains underexplored in MARL. This paper proposes a novel MARL approach, Adaptive Episode Length Adjustment (AELA), where the episode length is initially limited and gradually increased based on an entropy-based assessment of learning progress. By starting with shorter episodes, agents can focus on learning effective strategies for initial states and minimize time spent in dead-end states. The use of entropy as an assessment metric prevents premature convergence to suboptimal policies and ensures balanced training over varying episode lengths. We validate our approach using the StarCraft Multi-agent Challenge (SMAC) and a modified predator-prey environment, demonstrating significant improvements in both convergence speed and overall performance compared to existing methods. To the best of our knowledge, this is the first study to adaptively adjust episode length in MARL based on learning progress.

Shared autonomy imitation learning, in which robots share workspace with humans for learning, enables correct actions in unvisited states and the effective resolution of compounding errors through expert's corrections. However, it demands continuous human attention and supervision to lead the demonstrations, without considering the risks associated with human judgment errors and delayed interventions. This can potentially lead to high levels of fatigue for the demonstrator and the additional errors. In this work, we propose an uncertainty-aware shared autonomy system that enables the robot to infer conservative task skills considering environmental uncertainties and learning from expert demonstrations and corrections. To enhance generalization and scalability, we introduce a hierarchical structure-based skill uncertainty inference framework operating at more abstract levels. We apply this to robot motion to promote a more stable interaction. Although shared autonomy systems have demonstrated high-level results in recent research and play a critical role, specific system design details have remained elusive. This paper provides a detailed design proposal for a shared autonomy system considering various robot configurations. Furthermore, we experimentally demonstrate the system's capability to learn operational skills, even in dynamic environments with interference, through pouring and pick-and-place tasks. Our code will be released soon.

The era of artificial intelligence chatbots has opened wide in Korea. On the 10th, the domestic media introduced an artificial intelligence robot that helps the elderly. The human care robot developed by the Intelligent Robotics Research Division of the Electronics and Telecommunications Research Institute (ETRI) is the main character. The Electronics and Telecommunications Research Institute (ETRI) said, "We have developed a robot artificial intelligence technology that understands the elderly, responds emotionally, and provides personalized services tailored to the situation." According to ETRI, the development of human care service robots requires data to recognize people from the robot's point of view and artificial intelligence technology necessary for deep learning.

A Korean research team developed a technology for detecting humans lying on the road in real-time. Therefore, preventing safety accidents in the city and rapidly responding to it will be possible, and a safer society is expected to be accomplished. The Electronics and Telecommunications Research Institute (ETRI) announced that it applied the technology of Visual AI ‘DeepView’ to Daejeon Metropolitan City in earnest to prevent safety accidents in the city and promptly respond to them. DeepView is an AI technology recognizing human behavior. It detects people lying on the road through surveillance cameras in real-time. As it can be applied to preventing safety accidents caused by drinking, fainting, etc. and performing prompt emergency rescue measures. It is expected to become the core technology for making a safe city.

If you want to make changes to the way your face, hair, and clothing appear in photos you've posted online, help could soon be at hand. Artificial Intelligence (AI) software under development in South Korea lets you make alterations with the flick of a finger. Users can draw the changes they want - such as higher eyebrows, a different jawline or new earring - onto images directly. The system interprets these doodle to make realistic changes to the photo underneath. The new system, published in a scientific paper, allows users to change photos by doodling onto them.

If you want to make changes to the way your face, hair, and clothing appear in photos you've posted online, help could soon be at hand. Artificial Intelligence (AI) software under development in South Korea lets you make alterations with the flick of a finger. Users can draw the changes they want - such as higher eyebrows, a different jawline or new earring - onto images directly. The system interprets these doodle to make realistic changes to the photo underneath. The new system, published in a scientific paper, allows users to change photos by doodling onto them.