[Research] [Research] Professor Hongwook Woo's lab (CSI lab), AAAI 2024 paper was approved by publication (3 papers)
- 소프트웨어융합대학
- Hit600
- 2023-12-26
Three papers from the CSI Research Institute (Director: Woo Hong-wook) have been accepted by The 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024). The paper will be presented in Vancouver, Canada, in February 24.
1. The paper "SemTra: A Semantic Skill Translator for Cross-Domain Zero-shot Policy Adaptation" was written by Shin Sang-woo (M.D.), Yoo Min-jong (PhD), and Lee Jung-woo (undergraduate program).
This study is about Zero-Shot adaptation technology, which enables embedded agents such as robots to quickly respond to changes in their surroundings without further learning, and we present a SemTra (SemTra) framework that transforms multimodal data such as vision, sensor, and user commands into semantically interpretable skills (Sematic Skill), optimizing these skills to the environment and executing them in successive actions. SemTra has shown high performance by being tested in an autonomous environment with robots such as Meta-World, Franka Kitchen, RLBench, and CARLA, as a result of a study that transforms implicit behavior patterns into actionable skills (Skill, Continuous Behavior patterns) through pre-trained language models.
2. The paper "Risk-Conditioned Reinforcement Learning: A Generalized Approach for Adaptation to Varying Risk Measures" was written by Yoo Kwang-pyo (doctoral program) and Park Jin-woo (master's program) researchers from the Department of Software.
This study proposes Risk Conditional Reinforcement Learning (Risk Conditional Reinforcement Learning), which can be used in applications that require significant decision-making involving risks, such as finance, robots, and autonomous driving. In particular, to cope with various dynamically changing preference risk levels through one learned reinforcement learning model, we implement a structure of a weighted value-at-risk (WV@R) based reinforcement learning model that enables a single representation of heterogeneous risk metrics for the first time, enabling flexible processing of reinforcement learning-based decision-making in a number of risk management-focused applications.
3. The paper "Robust Policy Learning via Offline Skill Diffusion" was written by Kim Woo-kyung (doctoral) and Yoo Min-jong (doctoral) researchers in the software department.
In this work, we present DuSkill (Offline Skill Diffusion Model), a new offline learning framework that uses the Diffusion model to create a variety of embodied agent skills that are extended from the finite skills of the dataset. The DuSkill framework enhances the diversity of offline learned skills, accelerating the RL Policy Learning process for multi-tasks and heterogeneous environmental domains, and improving the robustness of the learned policies.
The CSI lab is conducting research on network and cloud system optimization and embedded agents such as robot and drone autonomous driving using machine learning, reinforcement learning, and self-directed learning. The research in this AAAI 2024 paper is underway with the support of the People-Centered Artificial Intelligence Core Source Technology Project (IITP), the Korea Research Foundation's Personal Basic Project (NRF), and the Graduate School of Artificial Intelligence.
우홍욱 | hwoo@skku.edu | CSI Lab | https://sites.google.com/view/csi-agent-group