Woo Kyung Kim

I am currently a PhD student at Computer Systems Intelligence (CSI) Lab in SungKyunKwan University, advised by Honguk Woo. My research areas include skill-based reinforcement learning, diffusion model, embodied agent.

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In this work, we present an in-context policy adaptation (ICPAD) framework designed for long-horizon multi-task environments, exploring diffusion-based skill learning techniques in cross-domain settings.

In this paper, we present a novel LLM-based policy adaptation framework LDuS which leverages an LLM to guide the generation process of a skill diffusion model upon contexts specified in language, facilitating zero-shot skill-based policy adaptation to different contexts.

We intrudocue IsCiL, an adapter-based continual imitation learning framework that incrementally learns sharable skills from different demonstrations, enabling sample efficient task adaptation using the skills.

In this paper, we present Pareto inverse reinforcement learning (ParIRL) framework in which a Pareto policy set corresponding to the best compromise solutions over multi-objectives can be induced.

We present DEDER, a framework for decomposing and distilling the embodied reasoning capabilities from large language models (LLMs) to efficient, small language model (sLM)-based policies.

We present a novel offline skill learning (DuSkill) framework which employs a guided Diffusion model to generate versatile skills extended from the limited skills in datasets, thereby enhancing the robustness of policy learning for tasks in different domains.

Wwe present a novel contrastive prompt ensemble (ConPE) framework which utilizes a pretrained vision-language model and a set of visual prompts, thus enables efficient policy learning and adaptation upon environmental and physical changes encountered by embodied agents.

In this paper, we explore the compositionality of complex tasks, and present a novel skill-based imitation learning (OnIS) framework enabling one-shot imitation and zero-shot adaptation.

In this paper, we present a transferable RL model Repot in which a policy trained in an easy-to-learn network environment can be readily adjusted in various target network environments.