3 citations · 5 across the 14 of their papers we have counts for
14 papers
RoboBRIDGE: A Modular Framework for Bridging Policies to Robust Real-World Robotic Agents
Sihyung Yoon, Minjong Yoo, Sanghyun Ahn +2
Vision-Language-Action (VLA) models have attracted growing interest as a scalable approach to robotic manipulation. While these models are effective action predictors, deploying th…
Test-Time Mixture of World Models for Embodied Agents in Dynamic Environments
Jinwoo Jang, Minjong Yoo, Sihyung Yoon +1
Language model (LM)-based embodied agents are increasingly deployed in real-world settings. Yet, their adaptability remains limited in dynamic environments, where constructing accu…
Exploratory Retrieval-Augmented Planning For Continual Embodied Instruction Following
Minjong Yoo, Jinwoo Jang, Wei-jin Park +1
This study presents an Exploratory Retrieval-Augmented Planning (ExRAP) framework, designed to tackle continual instruction following tasks of embodied agents in dynamic, non-stati…
In-Context Policy Adaptation via Cross-Domain Skill Diffusion
Minjong Yoo, Woo Kyung Kim, Honguk Woo
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…
World Model Implanting for Test-time Adaptation of Embodied Agents
Minjong Yoo, Jinwoo Jang, Sihyung Yoon +1
In embodied AI, a persistent challenge is enabling agents to robustly adapt to novel domains without requiring extensive data collection or retraining. To address this, we present…
Embodied CoT Distillation From LLM To Off-the-shelf Agents
Wonje Choi, Woo Kyung Kim, Minjong Yoo +1
We address the challenge of utilizing large language models (LLMs) for complex embodied tasks, in the environment where decision-making systems operate timely on capacity-limited,…
Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation
Daehee Lee, Minjong Yoo, Woo Kyung Kim +2
Continual Imitation Learning (CiL) involves extracting and accumulating task knowledge from demonstrations across multiple stages and tasks to achieve a multi-task policy. With rec…
Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning
Minjong Yoo, Sangwoo Cho, Honguk Woo
Reinforcement learning (RL) with diverse offline datasets can have the advantage of leveraging the relation of multiple tasks and the common skills learned across those tasks, henc…
Pareto Inverse Reinforcement Learning for Diverse Expert Policy Generation
Woo Kyung Kim, Minjong Yoo, Honguk Woo
Data-driven offline reinforcement learning and imitation learning approaches have been gaining popularity in addressing sequential decision-making problems. Yet, these approaches r…
Offline Policy Learning via Skill-step Abstraction for Long-horizon Goal-Conditioned Tasks
Donghoon Kim, Minjong Yoo, Honguk Woo
Goal-conditioned (GC) policy learning often faces a challenge arising from the sparsity of rewards, when confronting long-horizon goals. To address the challenge, we explore skill-…
Model Adaptation for Time Constrained Embodied Control
Jaehyun Song, Minjong Yoo, Honguk Woo
When adopting a deep learning model for embodied agents, it is required that the model structure be optimized for specific tasks and operational conditions. Such optimization can b…
Robust Policy Learning via Offline Skill Diffusion
Woo Kyung Kim, Minjong Yoo, Honguk Woo
Skill-based reinforcement learning (RL) approaches have shown considerable promise, especially in solving long-horizon tasks via hierarchical structures. These skills, learned task…
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill
Sangwoo Shin, Daehee Lee, Minjong Yoo +2
One-shot imitation is to learn a new task from a single demonstration, yet it is a challenging problem to adopt it for complex tasks with the high domain diversity inherent in a no…
SemTra: A Semantic Skill Translator for Cross-Domain Zero-Shot Policy Adaptation
Sangwoo Shin, Minjong Yoo, Jeongwoo Lee +1
This work explores the zero-shot adaptation capability of semantic skills, semantically interpretable experts' behavior patterns, in cross-domain settings, where a user input in in…